{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "10272034",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import pandas as pd\n",
    "from torch.utils.data import Dataset\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b502b02b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Classifier(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        # 定义神经网络层\n",
    "        self.model = nn.Sequential(\n",
    "            # 是一个从105节点到50节点的全连接层\n",
    "            nn.Linear(105, 50),\n",
    "            # 激活函数\n",
    "            nn.Sigmoid(),\n",
    "            # 是50个节点映射30个节点的全连接映射\n",
    "            nn.Linear(50, 30),\n",
    "            nn.Sigmoid(),\n",
    "        )\n",
    "        # 损失函数\n",
    "        self.loss_function = nn.BCELoss()\n",
    "        # 创建优化器,使用简单的梯度下降算法\n",
    "        self.optimiser = torch.optim.SGD(self.parameters(), lr=0.01)\n",
    "        # 用来记录训练进展的计数器和列表\n",
    "        self.counter = 0\n",
    "        self.progress = []\n",
    "\n",
    "    # 将信息传入神经网络\n",
    "    def forward(self, inputs):\n",
    "        return self.model(inputs)\n",
    "\n",
    "    def train(self, inputs, targets):\n",
    "        outputs = self.forward(inputs)\n",
    "        # 计算损失值\n",
    "        loss = self.loss_function(outputs, targets)\n",
    "        # 梯度归零, 反向传播, 并更新权重\n",
    "        self.optimiser.zero_grad()\n",
    "        loss.backward()\n",
    "        # 使用梯度更新神经网络的可学习参数\n",
    "        self.optimiser.step()\n",
    "        self.counter += 1\n",
    "        if self.counter % 10 == 0:\n",
    "            self.progress.append(loss.item())\n",
    "        # 了解训练的快慢\n",
    "        if self.counter % 1000 == 0:\n",
    "            print(\"counter=\", self.counter)\n",
    "\n",
    "    # 画图\n",
    "    def plot_progress(self):\n",
    "        df = pd.DataFrame(self.progress, columns=['loss'])\n",
    "        df.plot(ylim=(0, 1.0), figsize=(16, 8), alpha=0.1, marker='.', grid=True, yticks=(0, 0.25, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2a435de",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MnistData(Dataset):\n",
    "    def __init__(self, csv_file):\n",
    "        self.data_df = pd.read_csv(csv_file, header=None)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        # 获取图像标签\n",
    "        label = self.data_df.iloc[index, 0]\n",
    "        # 独热编码\n",
    "        target = torch.zeros(30)\n",
    "        target[label] = 1.0\n",
    "        data = self.data_df.iloc[index, 1:].values\n",
    "        data = data.tolist()[0]\n",
    "        data = json.loads(data)\n",
    "        hand = torch.FloatTensor(data)\n",
    "        return hand, target\n",
    "\n",
    "    def data_show(self, index):\n",
    "        arr = self.data_df.iloc[index, 1:].values\n",
    "        print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b710c19d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[0.5032, 0.3484, 0.2021, 0.0951, 0.0, 0.2789, 0.2684, 0.2699, 0.2584, 0.3608, 0.356, 0.3614, 0.3595, 0.4377, 0.4281, 0.4331, 0.4326, 0.5088, 0.5046, 0.503, 0.5008, 0.3176, 0.2061, 0.1439, 0.1783, 0.2584, 0.0446, 0.0251, 0.0136, 0.0, 0.1105, 0.1001, 0.1044, 0.1021, 0.1882, 0.1746, 0.1779, 0.1763, 0.2614, 0.2532, 0.2502, 0.2469, 0.2812, 0.2214, 0.2231, 0.2775, 0.3595, 0.0967, 0.0937, 0.0898, 0.1021, 0.0497, 0.0096, 0.0066, 0.0, 0.1003, 0.0755, 0.0768, 0.0744, 0.1697, 0.1543, 0.1499, 0.1457, 0.2759, 0.2579, 0.2891, 0.3499, 0.4326, 0.1669, 0.1664, 0.1638, 0.1763, 0.0919, 0.0767, 0.0722, 0.0744, 0.0672, 0.0214, 0.0105, 0.0, 0.1128, 0.0843, 0.0775, 0.072, 0.2869, 0.303, 0.3527, 0.4174, 0.5008, 0.2359, 0.2363, 0.2341, 0.2469, 0.1557, 0.147, 0.143, 0.1457, 0.0953, 0.0733, 0.0692, 0.072, 0.083, 0.0285, 0.0135, 0.0]']\n"
     ]
    }
   ],
   "source": [
    "dataset = MnistData(\"handtrain.csv\")\n",
    "dataset.data_show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4a12beb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch= 1\n",
      "epoch= 2\n",
      "counter= 1000\n",
      "epoch= 3\n",
      "counter= 2000\n",
      "epoch= 4\n",
      "counter= 3000\n",
      "epoch= 5\n",
      "counter= 4000\n",
      "epoch= 6\n",
      "counter= 5000\n",
      "epoch= 7\n",
      "counter= 6000\n",
      "epoch= 8\n",
      "counter= 7000\n",
      "epoch= 9\n",
      "counter= 8000\n",
      "epoch= 10\n",
      "counter= 9000\n",
      "epoch= 11\n",
      "counter= 10000\n",
      "epoch= 12\n",
      "counter= 11000\n",
      "epoch= 13\n",
      "counter= 12000\n",
      "epoch= 14\n",
      "counter= 13000\n",
      "epoch= 15\n",
      "counter= 14000\n",
      "epoch= 16\n",
      "counter= 15000\n",
      "epoch= 17\n",
      "counter= 16000\n",
      "epoch= 18\n",
      "epoch= 19\n",
      "counter= 17000\n",
      "epoch= 20\n",
      "counter= 18000\n",
      "epoch= 21\n",
      "counter= 19000\n",
      "epoch= 22\n",
      "counter= 20000\n",
      "epoch= 23\n",
      "counter= 21000\n",
      "epoch= 24\n",
      "counter= 22000\n",
      "epoch= 25\n",
      "counter= 23000\n",
      "epoch= 26\n",
      "counter= 24000\n",
      "epoch= 27\n",
      "counter= 25000\n",
      "epoch= 28\n",
      "counter= 26000\n",
      "epoch= 29\n",
      "counter= 27000\n",
      "epoch= 30\n",
      "counter= 28000\n",
      "epoch= 31\n",
      "counter= 29000\n",
      "epoch= 32\n",
      "counter= 30000\n",
      "epoch= 33\n",
      "counter= 31000\n",
      "epoch= 34\n",
      "counter= 32000\n",
      "epoch= 35\n",
      "epoch= 36\n",
      "counter= 33000\n",
      "epoch= 37\n",
      "counter= 34000\n",
      "epoch= 38\n",
      "counter= 35000\n",
      "epoch= 39\n",
      "counter= 36000\n",
      "epoch= 40\n",
      "counter= 37000\n",
      "epoch= 41\n",
      "counter= 38000\n",
      "epoch= 42\n",
      "counter= 39000\n",
      "epoch= 43\n",
      "counter= 40000\n",
      "epoch= 44\n",
      "counter= 41000\n",
      "epoch= 45\n",
      "counter= 42000\n",
      "epoch= 46\n",
      "counter= 43000\n",
      "epoch= 47\n",
      "counter= 44000\n",
      "epoch= 48\n",
      "counter= 45000\n",
      "epoch= 49\n",
      "counter= 46000\n",
      "epoch= 50\n",
      "counter= 47000\n",
      "epoch= 51\n",
      "counter= 48000\n",
      "epoch= 52\n",
      "epoch= 53\n",
      "counter= 49000\n",
      "epoch= 54\n",
      "counter= 50000\n",
      "epoch= 55\n",
      "counter= 51000\n",
      "epoch= 56\n",
      "counter= 52000\n",
      "epoch= 57\n",
      "counter= 53000\n",
      "epoch= 58\n",
      "counter= 54000\n",
      "epoch= 59\n",
      "counter= 55000\n",
      "epoch= 60\n",
      "counter= 56000\n",
      "epoch= 61\n",
      "counter= 57000\n",
      "epoch= 62\n",
      "counter= 58000\n",
      "epoch= 63\n",
      "counter= 59000\n",
      "epoch= 64\n",
      "counter= 60000\n",
      "epoch= 65\n",
      "counter= 61000\n",
      "epoch= 66\n",
      "counter= 62000\n",
      "epoch= 67\n",
      "counter= 63000\n",
      "epoch= 68\n",
      "counter= 64000\n",
      "epoch= 69\n",
      "epoch= 70\n",
      "counter= 65000\n",
      "epoch= 71\n",
      "counter= 66000\n",
      "epoch= 72\n",
      "counter= 67000\n",
      "epoch= 73\n",
      "counter= 68000\n",
      "epoch= 74\n",
      "counter= 69000\n",
      "epoch= 75\n",
      "counter= 70000\n",
      "epoch= 76\n",
      "counter= 71000\n",
      "epoch= 77\n",
      "counter= 72000\n",
      "epoch= 78\n",
      "counter= 73000\n",
      "epoch= 79\n",
      "counter= 74000\n",
      "epoch= 80\n",
      "counter= 75000\n",
      "epoch= 81\n",
      "counter= 76000\n",
      "epoch= 82\n",
      "counter= 77000\n",
      "epoch= 83\n",
      "counter= 78000\n",
      "epoch= 84\n",
      "counter= 79000\n",
      "epoch= 85\n",
      "counter= 80000\n",
      "epoch= 86\n",
      "counter= 81000\n",
      "epoch= 87\n",
      "epoch= 88\n",
      "counter= 82000\n",
      "epoch= 89\n",
      "counter= 83000\n",
      "epoch= 90\n",
      "counter= 84000\n",
      "epoch= 91\n",
      "counter= 85000\n",
      "epoch= 92\n",
      "counter= 86000\n",
      "epoch= 93\n",
      "counter= 87000\n",
      "epoch= 94\n",
      "counter= 88000\n",
      "epoch= 95\n",
      "counter= 89000\n",
      "epoch= 96\n",
      "counter= 90000\n",
      "epoch= 97\n",
      "counter= 91000\n",
      "epoch= 98\n",
      "counter= 92000\n",
      "epoch= 99\n",
      "counter= 93000\n",
      "epoch= 100\n",
      "counter= 94000\n"
     ]
    }
   ],
   "source": [
    "c = Classifier()\n",
    "flag = 100\n",
    "# 训练神经网络\n",
    "for i in range(flag):\n",
    "    print(\"epoch=\", 1+i)\n",
    "    for data, target in dataset:\n",
    "        c.train(data, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c9c157f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pNbq9WlOrZtlmBgAAAAAWhGR1Di5EbXUKdYdeW52hSkeJLwAAAADMi4B6DqNtZoyatZRtZgAAAABgQQio55BaycWg7U7BNjMAAAAAsCAkqzkYwzYzAAAAALAoBNRzcGG0cu8628wAAAAAwMKcKaAaY/6RMeZ7xpgfGGP+5ZTjrxhjDowxbxz996/O+tyrKLWjCzcoHNvMAAAAAMCCpKd9gzEmkfQlSf9Q0oakrxlj/jzG+N0nvvWvYoz/9JzPvVJSa3SrXdNOr2SbGQAAAABYkLMkq89J+kGM8a0YYynp30r6Z2f8++d57tJyIWq7W6pXOLaZAQAAAIAFOXUEVdJdSfePPd6Q9LNTvu+/NcZ8U9K7kv7XGON3ZniujDFflPRFSbpz545ee+21MzTtchx0etr4+tclSbVEWq9b1VNzya0Cput2u0v9egKexD2Lq4T7FVcN9yyW3VkC6rTk9eTyta9L+niMsWuM+SeS/g9JP3HG546+GOMfSfojSfrMZz4TX3nllTM07XL8P//h/9UnP/nTOhhWurNa00s325T5Ymm99tprWubXE/Ak7llcJdyvuGq4Z7HszpKqNiR99NjjFzQaJX0kxngYY+we/fkvJGXGmJtnee6VZSQpKgZGTgEAAABgEc4SUL8m6SeMMS8ZY3JJvyTpz49/gzHmOWOMOfrz547+3p2zPPcqGm8zc71VV5YYtpkBAAAAgAU4tcQ3xuiMMf9C0r+XlEj6NzHG7xhj/vnR8T+U9D9J+l+MMU7SQNIvxRijpKnPvaBz+dCkVnI+an9Q6la7xjYzAAAAALAAZ5mDOi7b/YsnvvaHx/78ryX967M+91lgHpX4XnZLAAAAAODZwNDfObggpYmhxBcAAAAAFoiAeg7jEt/dXqEyBEp8AQAAAGABSFbnNFq7N0qs4gsAAAAAC0FAPQcXpDQdlfimKSW+AAAAALAIBNRzSK1kZTQonSRR4gsAAAAAC3CmVXzxuNQa3V1vSqav1Xp22c0BAAAAgGcCAXUOBwMnH6IGpdfd9abylJFUAAAAADgvEtU5OR+kGNXMU0WJeagAAAAAMCcC6jnlaaIqRG13CjnPVjMAAAAAMC9S1TmZo/9ijJfdFAAAAAB4JhBQz6nyQak1urFSU5pYSnwBAAAAYE4E1HPKU6sqRO10S0p8AQAAAGABSFXnZIyRkSRR4gsAAAAAi0BAPafqaBXfepawii8AAAAALAAB9ZxilLZ6pe7vDbR5MBRrJQEAAADAfNLLbsBVZYx0q52rlWeqpVbGXHaLAAAAAOBqYwT1nLLEysioX3iZo8cAAAAAgPNjBHUO1hhFFkkCAAAAgIVg2O+cKh+UGKP1Vs4+qAAAAACwAATUc8oSKx+jtjvsgwoAAAAAi0CqmoMxRjKU+AIAAADAIhBQz6nyQakxutGqUeILAAAAAAtAQD2nUYlv0FanoMQXAAAAABaAVDUHSnwBAAAAYHEIqOdU+aDUWl1v1inxBQAAAIAFIKCeU5ZY+RC00x1S4gsAAAAAC0CqmhclvgAAAACwEATUcxqX9DbSTPHYYwAAAADA+RBQzylGabtb6MFeX5sHQ0UGUgEAAABgLullN+CqMka6tVLXoO7USBNGUAEAAABgTgTUc8oSq0HltLE3ULuWqVVL1aqlylMGpQEAAADgPEhTc6h8VBWcXAwqfWAUFQAAAADmQEA9p8oHxRA0KKIe7g30cH/APFQAAAAAmAMB9ZxilHb6pVzllaVGN1t1GXPZrQIAAACAq4s5qOdkjPSx6y1JUvSStaN5qQAAAACA8yGgnlOWWLXrqSSjWmqVWsIpAAAAAMyDVHVOeWp1u13XejPVR282laaGRZIAAAAAYA6MoM4hTxN1iqDKF2pmVnfXmpfdJAAAAAC4sgioczBGut3O1a5lylLLIkkAAAAAMAdKfOeQJVYyRr3Sy4hFkgAAAABgHiSqOVkZybABKgAAAADMi4A6h8oHxRjVSFPFo8cAAAAAgPNhDuocYpS2u4WMSq3WUxZJAgAAAIA5EFDnYIx0Z7WuLLFq5AmLJAEAAADAHCjxncN4USQWSQIAAACA+ZGo5mStkcTcUwAAAACYFwF1DpUPCiGqkWUskgQAAAAAc2IO6hxilHa6hcoqar2VsUgSAAAAAMyBgDoHY6S1Zq4qBq03chZJAgAAAIA5EFDnEKN0MChVlEHy0os3LrtFAAAAAHB1EVDnYIy01shV1oLWm4ygAgAAAMA8WCRpDjFKO71CD3cH2joYKsbLbhEAAAAAXF0E1DlUPihGqZYncjGyii8AAAAAzIGAOqfRPqhSZPgUAAAAAOZCQJ1DllhZIw2dkzFGWcLlBAAAAIDzYpGkORgjPb/WVLuRKTMJJb4AAAAAMAeG/OYwHkHdOix0UJTa6gxVOkIqAAAAAJwHAXUOeWp1q11Xu57oTruuKDGKCgAAAADnRInvnGppok4R5MJQjdTq7lrzspsEAAAAAFcSAXVO1ki32rnWGrkSa2TMZbcIAAAAAK4mSnznlKdWklG/8DISK/kCAAAAwDmRpuZkjNHRTqiX3BIAAAAAuNoIqHOqfJBiVD1LWCQJAAAAAOZAQJ1TjNJWr9S93YE2D4aKDKQCAAAAwLmwSNKcjJFutDKlJlGrnrBIEgAAAACcEyOoc4pR2jostLHX1xYjqAAAAABwbgTUOVU+yBij1Br1nVe/dJfdJAAAAAC4kijxXQDngvaHlWKUHuz1tdbMj7afAQAAAACcFSlqTlliFY00KCvJSN3CqVcwigoAAAAAsyKgzskY6bl2XdfbdQ0rp71epa3OUKVjuxkAAAAAmAUBdU4xSr3KST4qeOnWao39UAEAAADgHAioczJGWmvkurlaU5IYbR6wHyoAAAAAnAeLJM0pRulgWGm/W0lRWl/Jtd7K2Q8VAAAAAGbECOqcxiOoayuZZKSdbqm9bskIKgAAAADMiIA6pxilg0H5wQhqK9P6CiOoAAAAADArAuqcjJGut2ofjKD2GEEFAAAAgPNgDuqcYpS2OkNtHQ7lfND1Zs4IKgAAAACcAyOoc6p8kDFSnlkdDivd3+1pi1V8AQAAAGBmBNQFSJPRZfQhyppEwbAPKgAAAADMioA6p1Yt1c1WTf3KK7FWvdLJEU4BAAAAYGbMQZ1Tnlo9v9bQg71cgyKoqJx8iMoSsj8AAAAAzIIUtQC1NFG3jIo+KLGJbrbqLJIEAAAAADMioC6AtdJaI5G3RkVVaafHIkkAAAAAMCtKfBfC6L1Ood3uQHmSKgQWSQIAAACAWTGCugDjMJolqaIkFxg+BQAAAIBZMYK6IIkxCiFo4LxcCCySBAAAAAAzIkUtQJZYlT5o4IMaWaI8SSjxBQAAAIAZMYK6AJUPSqxRKxtdThcIpwAAAAAwK0ZQFyQxRkZRpfeSRIkvAAAAAMyIEdQFyBKrEKPSzGqlnupGO6fEFwAAAABmxDDfAlQ+KE8TXatlOhhUem9vqHf3+yodIRUAAAAAzoqAuiCZtWrkiXyIatRS7XRL9Qp32c0CAAAAgCuDgLoAWWIVQlS/cpKMYoxyR3NRAQAAAABnQ0BdgMoHpanReiNXVNROr5ALkYWSAAAAAGAGLJK0IM0sk/dRNWu12kiVJJaFkgAAAABgBgzxLUCrlurFm009d62hdiNXDEbeUeILAAAAALMgoC5Anlq9eHNFL95sSYq6t9/T+4eFFC+7ZQAAAABwdRBQFyRPR5fSWqN2LZeXjhZNAgAAAACcBXNQF2y3WyjKqJWT/QEAAABgFmdKUcaYf2SM+Z4x5gfGmH855fj/bIz5z0f//UdjzKePHfuRMeZbxpg3jDFfX2Tjl06UfAyqgpfzgRJfAAAAAJjBqSOoxphE0pck/UNJG5K+Zoz58xjjd49929uSfi7GuGeM+ceS/kjSzx47/g9ijNsLbPdSqkJQnqZKrVUMgRJfAAAAAJjBWUZQPyfpBzHGt2KMpaR/K+mfHf+GGON/jDHuHT38/yS9sNhmXg3NPNXtdl2V8wqStrulSsdWMwAAAABwFmcJqHcl3T/2eOPoayf5dUn/57HHUdL/ZYz5hjHmi7M38epYa+b6u8+1lWdWt1frOhyU6hWMogIAAADAWZxlkSQz5WtTZ1caY/6BRgH1fzj25f8+xviuMea2pP/bGPNmjPGrU577RUlflKQ7d+7otddeO0PTLke32z2xfZu9Sg/fderWrDZ+ZDTYSLRWTz7cBgLHPO1+BZYR9yyuEu5XXDXcs1h2ZwmoG5I+euzxC5LeffKbjDF/X9L/Lukfxxh3xl+PMb579P9NY8y/06hkeCKgxhj/SKO5q/rMZz4TX3nllbOfxYfstdde00nt2+kUim++p0aaqJZZfe6lm1pv5R9uA4Fjnna/AsuIexZXCfcrrhruWSy7s5T4fk3STxhjXjLG5JJ+SdKfH/8GY8zHJP2ZpF+JMX7/2Ndbxpj2+M+S/kdJ315U45dRLUtkJBWll5GUJWw3AwAAAABnceoIaozRGWP+haR/LymR9G9ijN8xxvzzo+N/KOlfSboh6X8zxkiSizF+RtIdSf/u6GuppD+NMf7lhZzJkqj8aFGkLEsUjz0GAAAAADzdWUp8FWP8C0l/8cTX/vDYn78g6QtTnveWpE8/+fVnWeWDOkOnRhZVOkNABQAAAIAzOlNAxdllidVaI5dM1EotpcQXAAAAAM6I9LRgWWJljDRwjjmoAAAAADAD0tOCVT7IRKluU+agAgAAAMAMKPFdsMoH7Q4qhRBVek9ABQAAAIAzYgT1AoQYVXgnF+JlNwUAAAAArgxGUC9AmhhFWVnyPwAAAACcGQF1wbLE6larpn5VaSXPWSQJAAAAAM6IgLpgWWKVpUaDgZeJlSrHHFQAAAAAOAuG9xbMGGm9WdOwDNrtO33jnR11h+6ymwUAAAAAS4+AumBZYuV80PZhIR+CNruFDgblZTcLAAAAAJYeJb4LlqdWH1mrK8ao0gfFQZRYzBcAAAAATkVAvQBrzZo+8dyKEkntvKZmjcsMAAAAAKehxPcCZIlVbhPFaJSmYiVfAAAAADgDktMFqHyQkZQlieLRYwAAAADA01F7egEqH7Q3qKQo9Uqjg0Gp1lGZb+XDaIQ1pW8AAAAAAI4joF6QEKKGldN+L2q7U6h0Qc5H1fNERtLd9SYhFQAAAACOIaBeEGOMXIjqVk79yilK6gxLPXetqcQaVT4QUAEAAADgGALqBTFm9P/Dfqm/3ezqRiPTwAXt97zWm6nurjUvt4EAAAAAsGQYwrsgRkY+RPkgKUqF9xpWQVFRZQgsnAQAAAAAT2AE9YJkdjSE2ikr7fYKDatU/dLJh6jCWQIqAAAAADyBEdQLkCVWN1dqijGqqqI2DwYqq3hU9hslmUtuIQAAAAAsHwLqBWjVUq01Mm12ClU+aGfgVHqvtWaua41UN1ZyZQmXHgAAAACOIyVdgDy1urVak4lSkhgNhk4+jEp6B87LSARUAAAAAHgCKemCNPNUSWLkvB8V9UajKKmepIoSc1ABAAAA4AksknRBstRqtZ6rnqYyZhRO93ulFIwcq/gCAAAAwARGUC9QmhgliWQTo6ioEKMK7+RCvOymAQAAAMDSIaBeoBikGI2spGS0hK98iBqUjKACAAAAwJMIqBek8kG90imxkrXSajNTjNJur9T7B0Pd2+2rdIRUAAAAABgjoF6QygfVs0Q323XVUqt2I1FqrQaFV7estLHTU69wl91MAAAAAFgaBNQL0sxS3V6tabWW6GarrrVapmutTDJRPkQ9PBiqT0AFAAAAgEdYxfeCrLVy/f276/penupaPdWtdl1VjHo7sSqqoP1+qXt7fd1arStP6ScAAAAAAJLRBclTq5+8e02fvntNK7VEpQu61a5rJc8kRQ1Kr4d7/cfKfEsX1Cscc1MBAAAA/FhiBPUCrdRTPb/W1HfePVBmrA6rSpUPOhiWKqpEW93y0Wq+pQt6Z6erxFoZSXfXm4ysAgAAAPixQgK6YMPK6bBfqYxe2/uFtg+H6hWV9gel+qU79n1eW51iNILq2YYGAAAAwI8fRlAvmJHRZqfQdrfQbq+UD5ILUhWC9nofjKCGo4WTHuwMdLOd6+5a85JbDgAAAAAfLjUHm8IAACAASURBVEZQL5qRgokqfJAUFRXkQ5RNrKz0KKD6GOVCkExUGRhBBQAAAPDjhxHUC1b5oExWtZpUOq+2ydQYVLLGKEmNMjvqIyhd0H5v9PWKgAoAAADgxxAB9YJl1soralB4SdKLt1ZUhSAXpRsrNWXHFkJyMSiGqFrILqu5AAAAAHBpCKgXzUiJMTLRKEusPrJa0063philesrlBwAAAIAxEtIFq3xQniayidQdVlqpp7rVrqtfOlUuqF869Qp3ppLeYenlwujvYwsaAAAAAM8aAuoFa2aprjUy/e37fVUh6p29gYykQeU0LIO+/vauPvXCNR0MKnkfFRVVuMk5qKULev3+nqyMrrcyvXhzhZAKAAAA4JlCQL1ga61cn7jZ0g+2OrrRTNQfBq3UrRI7WiDp7Z2u2s1UtcSqdEH9yumg7/TDza6uNXKt1Ee/ol7h9HCvrzRJ1C8q3WrXlaf5JZ8dAAAAACwOAfWC5anVx2405UqvvRjVL4J+9sa6BqVXnqV68+GBova1UstG28xE6WBQ6YdbHV1rZPp7z19TnlpVPmi7W8oao17NTi0JLo9GXrPEMroKAAAA4MohoH4IstSqWU8la1W3Vp+801ajluqgX6qZJ1qtZwpBatczbR72tdcvtLE70J12Xx+70Xo0Uvq0VX5LF/Tmw0OFGNXME0qAAQAAAFw5BNQPQ5S6hZMLQc08U54kyqyV80GdvtN2XqqRWD2/XtdON5Ek7fcLPTgYPjZSmlgja4xSOxk8e4XT29td1bNUjcxSAgwAAADgymGI7UNQhSDFKMkq+KidXqHvv9/Rdq9Sp6wUYtS1Zq471+pKrVVipW7hVXn/6O9IrdF6M9daI9ONlVxZMvmrcz5qWDm5Y88DAAAAgKuCEdQPQeWCiiClNqoIXt1hpc3DQiFEdQdO0miv1MxaJYmRD1Ey8bG/I0usjKSh81o7evzkcR+DekOnRtaYGmCLyj/a9obyXwAAAADLhoD6IWjmqT5+o6UYpcRK9TRRp3DqDEsNvJcPR1vLhCAfoiofVSmOguoR54OipHqSql9UenjQ10fUfLTKb3XseDx6fNx4m5rKBT23Wp86R/W0RZZYhAkAAADARSKgfghutuv6mY+uaVA6NfJU11dqWskT+ZCpZocaVkHdstJ7BwPtdIY6GJSy0WqnWzwKmqUP2u9Vcj7qwX5fMlbv7Q/1Mx+/rpV6qsoHdQZOeWKlfjURUHuF07u7o21qnAsTc1RLF/StB/vqF053pgTY0gVt7PXkfVSaWN1dbxJwAQAAACwUAfVDsFJP9d998pb6pVMzT7XdGaoMQWXlJGO1Vkv1kdWmFL2cD7LGSlbyIapfOvUKNxohNVH9qlKvdPI+qPRe/dJppZ4qS6zajVRGUdca2USJ72nb1PQKpx9tdZUmiaopAbbyQdudUsYYrTbSo1LhxwPs99471H6/1O3Vml662Z44fn+3p17ptVJL9MJ661JCKiEaAAAAWF4E1A/JSj19VI57MLD62HpL7xqr0nuV3qldT2VklVorH6OSKJkg3dvujVYA9kGr9VS9YaX9fqmv/WhXP3n3mv7rj92Q9Pgc1dUwOUdVkqKJiiZKSiaOVT5or18pT7yKajLAxiht7PXlXNDNdq67a83HjvcKpx9t95SniR7sDXS73ZgIuL1yVM6816u03nRTVxmeN0AOSy8Xps+zLV3Qvd2eTJSsNROjwKUL+tFOV4qj6zdtlPg0iwjAlx2i5/35yxDyl72Ny96+s7Thstu4iNfSRVv2a3gWV6GNAIBnCwH1Elxr5FpvpfrOxlD1LFWWpPr4jYZkjG60D7XWLRViUJpavbXbU7cMqidSaqXNTiFJOuiXMkaSGf2dx+egVt7rYFAqTz/4QJElVuvNXEZR7drkKsDHR2DbtekjsDJSLU/k4+QcV0myxsh5rypMHs+S0bY6b291lSWJ6plVq5Y+9oFnUHr9p/t7qiVW7Xo6UWZcVF7fuLen1BitNbOpZciv39+TonRzJZ84XvmgzcOhsiSZOgo8GiUuTjw+/hknfVg7rQx6PIpcuKBGlkwNwMPS67sPD7Raz5RMCdHja1RPrVZqk9fotDae5Rzu7fakqKk/v3RBP9ruyseoejp5DqUL+uFmV8PKa62ZXVgp+KD0CjGeeA4/3OwqT8yJHRHfe+9QWWpUS6b/HhYR0kvnT+woub/bU+nDidfw3m5P/dKrXUtP7Ci5yI6EcWeNlTnxPnhrqyuZeKHX8Gnte7DXV+mD8qdMOfjB+x3VMitrJs/hLOZt4/2j19JJ9+G93Z58iCeew7ztW8Q5vLPTlTXmUq7hIo4vwmk/w4WoXuGWtiNiGa4hAMyCgHoJVuqpfuJOW6+9ual6arXTKRU1+rBdy6yauVG3lA4GlfqbXt5HteuJ1lu5XBXUK5y6A+mHm131S6f1Vv5oDmqM0vffH6iRJ+rdWHkUYI6PsLZrcWpAfdpx6WhhYSOFGCeOtWqpbrRzvbXVUTPLtNUZaq2ZP/rHLk+trrdyvfmeVy1P9P7BUM+vNR8bRe0MK20dDtWqp+oVbqLMuF84bR2OQv2w8hPHe8eOOz9ZphyjtNUtpRA1LCdHgWOUHh4M5V3QrXZt4njpgr79YF/dwk1daKryQbvdSlFSu5ZMDcB7/UoxSi6EqQH4cFhpWHnliVWW2onvORyWj65Rdzh5jUoX9Lfvd5Qm5tGH3ifP4fV7u1I0ut6aDPlnCvHdUYi3DXPCOY6Ol37yHE8L6eMP7fu9SmutTB+7PlkK3iucvrmxf2JHxrDy2usXauapGvn030NnWClLEpkp5zAOZybqxI6GH26OQnorn34O/3ljT/3Sn3ifbHUKJdbK1DW1fbvdUTm9Ne7EjpK3t7uPXrcnhfB6ak88h6+/s3tiZ89pnTWVD9rtFU+9hvd3e0cLw51crVC5qOaUa+hC1FtbXXWGla63cn30ifug8kH90qv0QX25o/eaxysyOsNK+4NSqzGb+lqSRh0d/ikVF29tdZWYk++D//LwQMaYqR0JlQ96/5TX0tNea+Of8bSOjo290WtpWkeHNOrwevO9Q7Vr6dSQfFqnYOm8tjqFVmqZatn0a3jRHV4/2OxoUHpdb+XTfwfvHuhgWOnOlKklp7XvrOfw9nZXqZ0e0ksX9KAz6jCZVnkzfk+2Vif+ni66Q2xjr6fKT+8IGXf2DJ0/sX1ncdU7Iq5CdRPw44SAeknyJJEPRoPCywevykc1a1YfX2+pV3g92OsqzxId9gt1WjU1skT1LJHsaMEka410bKRyPAL6cL+vrU6hb28cyEc9CjCVD0oSq5bJNCi9trtD5ekH/xBVPig9Ol75+Ghu61iWWNnEqlsUMsqkJzJqnlrdatd0b6en1UaqnW6p59ceL+M1RuoXQUaV+kcfvI8zGu3lOii8bD4ZguPR8aGcMjtZpqxTjhsjrTdTyYz2lDXm8ePHR4mDmRwF7hVO93f7qmep3jsYTg3ADw8HCj5qtZnqheutx9sfpe3uUL2BU54l+uhaS63aZBsfHgz1/mGhtXo6EZKNzFOvUeWDDgblYx96nzyH9w9GIb50kyH/LCH+rCF/Wil45YP2eqMQH2Kc+qF9UHolidFer9KN1mQp+LgjY+WEjgwdtdGEQuut2TsizhLOxiE8SybDWa9werA3eOp9stktFH3UWjPTC+uT98lOb6j+cNSZc/dac+I+6RaVdrqF1hq5kilt6JdOnWEll6Vq1iZDcO+Uzp5574NR+CqUJvbEa7jTKZQmidIp7XdB2u+XSqzV8NH2WB8czxKrwnnd3+2rkadq5cOJioxxG/d7Tu16MtHGohqFM6uTO2vGIfykcxiNmiWqpZOdMfNew9IFfffdAx2eEL4qH7Tfr+SD5PKTO7z6pVNqzdSQflqn4LiN24fTX0vjTrvEWq3Wp4f0eTu89vvliR1evcLpnZ2+almih/vDiakl446SznA0jebJjg5p1PH5xsb+iVUpReW10y3UqqWqZ9M7vLpl0MHAnXiO4/fkaec47qwJcXo1wrhDrHBe1xqTVSmlC/rOg311CnfifbLXrxSC1Mwn74HKBw0qr8KN3nundfZIp1etvLU1CvEndUQ8LaSXLuiN+3vyIepGa7L6qXRB39rY16Dyut2unbty6Gkh/kwVGZsd1dLpFRnja5AlJ1cbXHaIP378oix7RwSuDgLqJckSq1piVYWgujFKE6tmnuoTd1ZUeqf9fqlmlmjTexWVkwu5Mmu11qipZvuy1ipJjRT1qLToejPXD97vaFg53dvrq9XI9TMf+yCgmCDt9CrtdgsZa1S68NgbvYnS/sApKOqdnd5jI6DGSLdXcjnv5YPRvd2eWvXHPxDWEqvEWDkfpegnzjlPErUbqfLEqpknJ+7luj8sVUsn93I9ba/X047HKG11SnkX5KugF2+sTP5igqREMlNGiaUPAnBipowwG+lGK5eLUe08mwjAxkhrjVyFC7LG6OHBYOIajvbAHf0jPi0k54mVsdLAO7VMbeo5Pi18HT+H84T4WUL+tFLw0QfegcoqqlVPJ34HWWIVQtRbmx2l1qqZT5aCP+rIKINMNvl7GrfRnLMjYt6Rdun0a3y9mSlIulabfp+sN2oq/VCJsVPvk9PC1wchvdR6K/vQ74NxCD8tnMUQNSintS9qp1doWHjVa4mefyKk56nV7XZN+/1C11u1R1trPXafHLUxTxKt1NOJNh6vyDhvZ83T7oN5r+G4QyxPE707ZV5/jNLm4VBFFdSspXrpRltPMmZ0DgcDp5V8yn2ip3cKnuX1Pqy8skRTA+RFd3hJkg9BwypKmnxPHo+0R0m7vUrXp3R4HR7r8JpWlXJaSI9R6lRR7+71z3WfHO8Qm1aNcFpVyvg+qWWp3psS0mOU3j8YqHJRK41UH5/ynlu6oHs7PTWyVCu1yc6e8Uj70youdnuFammqlfr0EP+0kN4rnB7ujzr1pi3S2CucNvaOOofDZKffOBz2S6/1KVNLjo+0327X9IlbkyH+TBUZ/VKr9ekVGYPSabd3ckfGuFrAmpND/A83uzL25I6K7793KB81tTNoHOL75ck7Mbz58FCHR9fAhcnX+1nC4bD08k/pqPjRdlfpUyp73trqKrMnT7+5t9uTka5EyCdAXywC6iUqvNcgBEXntdkZaK2Z65O327reyuWjFGPU/b2+hi5q+7DUR6875ZlVq5EqBmmllunNh/t6azvTndWa1lu5jIyCojpDp8p/EBJbtVTXmpl+uN1R6aPe3R/o7lrz0Ztoq5bqWivXg4O+1hqZNjvDoxV565LGwUHaPizUrKd673Cgj91oPfYmnqfJKGD2Cq21Jue55ulRGXHl1JoSUF04mkdrp+/l6o5Gga+lNSXJ5EJO1RmOPy2YHA+49RMCcohBB0OnPJkegPf7pQaFV6dW6cUbj4+QZsloESxXBTUaiYImRxAlSUGKiqOS6icYM5pfa6zRemP28JUdBdyhG40ozBrijx93lZ8a8sel4HFKyB91dDRUBC+ryd9RnlqttTL5GLVanz4Sn6fJKKS7Ss18ekj/oI2zd0TMO9J+/Bq38unXeL9fyfso54P+TmxPeb6Rq4KazZPvk6eFL2vNKKSfcJ+c5T7Y6zspRJkgvXhDE8efdp+cKZy1UkUZXatPhnTJ6Hoz12YoZDU9pOdpok4RVLrhqNR6otpg1EYFjUZIn7yXdbEVF2e5huPjCnHqfep8OJpSMb1D7Fa7rsJ7GZmJnz+6BqP7oJakatWTiXM4S4fXaedwkSH9tA6vcWXPsKrUyCbbP+7wurfTUz1NtNVIT+zwGp6zw8sYqZVI6+380jrEfIgaVm60A8CU9t9u11WGoOSE99xb7Zp2e0Ndb07v7OkVoxDfrJ1WcVGMSrEX3Kl32vHj1Q6Fm6y46BVO9/f6ypJED/YHurP6eIhfREXG+Byjj7qxMr2qZLtbPOqof1pHxEkdFYdHU1NO6qh4FOKnVO70Cqe3t7uqZ6ke+oGG7vF7fRwuDwbTp1VIo5D+rQcHJ1Yb9Eun7W5xYogvnddur1AjS9U8YRrU5uHwxOPjOfF6yvSb06YMvL3dVeHC1GkZpQv6/vuH2u9XutXOp+5G8dZWR52h17XG9DVAxt83T8h92mKfZzn+rIRoAuolqUJQu5Er9Avd75b6D999TzvdUj//Xz2v59ea+uj1ph4e9HW7latVSxTj6IXrfZQ1UkiMOgOnbz081J2VprY7hZ5fr0lGKl189I/kB2VoVneu1WRktD8Yardf6CPXGvrJ569JGv0j9dH1hjb2ejocOvWKoNvtD0ZR89Tq5moutxEVQtTm4fDR/NexccCsZdMD5qMy4zybGiBL98FernHKXq5ROnWEc57jxwOutZMf+MZl0KMAPHl8NDJW0/thOPVDdZ5afWStPnqDrIL2uuXEB778KCTvDZxutU4OX8EFufb5wtfNlVwyowAw64epM31gNEZD56aGx3EwergzUJYmateTiQ8CtTRRYqwqF5Q8WUt+7BwSa3WtMRluXLjYkC7pzNd4WvgyRrqzWlcVgjKTTP3AeHetrns7PRVVnHqfGI2Cg5VX5cKJAfKk++S0+2CR4Wxa+IpR2u6UqqqgYsXp79x6PKSnVrJm1Jmz0kqnhvRxG+vp6MPM5H0wqkaoZfbc98HTwpmkuTo6jh9fm3KfPB6+pi9sZ420dTj6wLfVmfxQbcxRSI+lrjcnQ/ppHV4fRkift8Pr7rW6ypArNSeEr9WadnpDrTfrU8PXuMOr7yo1Tujw+uBenn4Oh1XUe7uDc79fnDmkH70Ojht3aD39Pddoa380L3/afVJLE+31nfb7Pa03JqeW6NjUkqR2sZ16016LZ3mtvncwlHNBN1byiWkTozM4eRHHRVRk+Dh6v8nz6e83xzsirs9ZTXDekO+fUgFW+aDDgZNk1Cunr31wOBh1VKw2Tqg2CKNz2OlWWptyH83bkVH50foN80wZ2Dk6Pm1aRq9wemuzp1qWyPnJaoRRyO/JGqv9QTE5vUijKQHfefdA7Vo6NUSPF/s8ed7/aLFPI51Y7v76/T15H3VndbLcfTySXvk4derKVUJAvSTX6rlWa4ne3h6qO6j0zvZQMgf6bz5+XbU00XsHQ3WGXoWCwtDpZjNXtwwaVE4+jEYh9waFDgujdi1TNFbDKtXhoNSwcjKyKqqgd7Z72qkVqueJrDGSGb1AZKKG7vF5oCu1TD5Ebez3VU9TbR0O1Ss+GL1q5anWWpn2uqUSY/Xu/uDoBTq6+SsftN+rlNhKiTUTAVaSFKRg49R/qI+vJDxtL9d5S3xPGyEdt0/J9A9DkpTa0YfC6qgH88m/fzzy1WpO/1BdSxPduVb7/9l7syZJkutK81NbfQ2PLSOXAqoAAiDBBptk93T3jMiMyPyD/rvzPg/9MDIiIyAJElWoJbNyiYzNV9t1mwc183APN3OPyqwiUDOlL7VoZKSrupraPfecey5xEDBsCaqlcSB5EAatIL4BX9EjWOCPlTljdxmZQ3skBLw46aGM7QwYTwYhX1rLJPZbGdLNYKTXEphvgq9qHLeCnx8SpD+KIc0VstIkPcmnJ7tMuhBwOcuJQp9BvCtjjupzMghDepHXAhwcMxZ4Hkd7QHrXOflYhhQO73ETsLaBdKkNot5ja3eTPYEneHHS49tpStGRzBEIblYVAslJf1cuTn0OrPdh5+BQ0P1dAG4XSG/2uBwp/uoBSN8EX13M18VRj1lWdjJfzTnoKhloPqMwFvQPew4+mCE9lPASgptlySBuB1+RX4OvNOVk0FLXfyDh9Zg1WPvvkxDrBOnH/b0Jr4txzGIP+GrOSb/jnNyvQTLq9X7QpF7Xs9jMn7R8R5v3iWH38zcmjt/epRz1IuZZtVXC5PbpIxUZzXtHfHzC6mNB/ihuB/nGWpKiIvJ7PMQs1sJt2nhkeK0eGa5TgyWXhp6/u0bPc2v4mGfpY9UGP2TJQL0KlNYY1d7NIikVhdT4nqDH7rOW1KUl/ai97n/TH6JL7t7MdzHlTmbtk5WyFUT/WMZPAPXPNJ4c9fiff3XGm2nGPJOsSsW7eVZLJAq+uc0YRB4oCPtwPu5xsypJSoXFklUajOV6VeIJwdNxj08mfcBJH7R2MoCvbhIujvr0Q49h7FMpw11W4iF4eZOt29GENRhSxlLkhsIrebsoth7Aplbl3TJvBbDWOtny3aLE93y+nWZbALYBT7dZhbXRjtHSppPwMbu9XJW+b6XTxdA+dr4x6Nn5+z1IqgpP7H4+95KLeXnb7VT8yXGPr29WLDKJL2hlLG5WFcJKTgbdQbXpeMkdAl/fh8z5HnztBiKh7+Ht2SP384LLWUYU+gzjXYY0qhlSpQxey0sOXDAS+r7rD/wjZEhPByF31uLjtzLpJ4OIr23CqAOkN+fEE5Ljllpd1iDdUo53mafHgPQfMhhqGFKl2hlSgMDzEVgqs5vsAacm2AfS78HXfoY0V4phvF8KfkjO3iVjfizAbWNIt0B6i0T3UeAr8JhlikWetpqqre+TjqTgD818faeEWIda4vlJD21sJ/h6chSzyEvOR+0M6fqcdDDpmyDdaPthIL35vB+ZEOtiSL16j7tAOjgmfdjbz5CuioyjuL0WGeOM6/aVlnwfSb2PLSmQI80vz3fvE2qmva3LQBR4vDjuk5WS81F/L0P6sYqMXDmDye/7vgEe/V7qum+CQKxjg8rs/vmT2iNDdJZV1N+jVPSCfcljS9XxXvohE16Puc+aO/dDSgaGccDZMOTLmxWxH+zEgOASGZeLYp3IaLuTXUmBIhDfXc5+aN5JxSWRrynlbgz4Yxo/AdQ/04gCj//6i3O+uUl4s8rw7H2+RirDPCtZ5h65NPzyyYhnkx6ZUvQznxMTcbUoWJWSUltOhxH9yLVWgRrIWYHShkwqFlmJ6LtLVxsn0fU9j0prvrxacpdUjHsOtC1TRSolxsJmDStwEMCCq4VJS40QmstZRvrsaB14S22QxpDlGqtLXj0wWmokwoHwyKXecRKuaobWZdRdr9fNF7HU9xLhalXsnS9UuTPfXPCzTBD67cDiYhzxZurtcSoWRKHPKAw6JbLKGALhtQbmDYjPyoreB7DEwKNlzm0M6Rb46jDwOR9HTNPuPToZhLy8dS1Y2mtIazOszLWOaFvDzapa1/J0gS/7Z2RIm5ds0QK+GmChpCGIaGXSw+DeUMyKXUOx5jP2wg6pdQPS4+5gad85Mcb+oCB9c4/bGNJhHHB+gNEQwjGkgac4iv3Oz2htx1mvP2NX7aD6DnL2zmz+HnnqIYYUIKwVGU1N2eZwDKkDX2ejDuZL270M6Tpo3lNjeiho/r7UCGpPUnAvQ7qR8GpVG/gOpCdFyrgFfAnYe06aoLbznDwiIYb4YRnS58d956DbAdKfHvVYFRVPxu0gfX2fHErmaEU/6gYePyRDuk7mdDCk6++A7w4sYOOclCnjFobUq++bQ8njD1VkfJfymQ/xyHjMfbNOCtax3MPf7x/wyGjWeDh53H7ODt25/x4eGU0i41DJwD7Vysu7lMkwbI1vGqn3hyYyvg+l4D4l4o9p/ARQ/4zjZBTxP/3ijP/r5RSLxWhBXilO+jHWCpJKIozhyVHMyTDkMzPAQzDPKq6XBU/HA3LlegV6nmCe+QyjkF7gjAyWpWJ1teL8qMezcY/TcQ/fczeKMhYpDS+nOWllGcd1n1Vr6uDXw9rtGlapzRaArdR2YB36HqNegKkvodu02no4pTZMV5JFrljkznhh8wKslOFuVbHKJVJbTgYxgyjYAqlWWJQ13M4k396lW07EzYOpjWaVad7O8p35k1GE0orbpWr986K+oEdRu0S3Yf+0sbQ5FQMuE91hctTU2fZCH90CUNcSXxEizW67H2X2M6Tf5XKLO4yeZplCyVqeetomT92/R/GBPVLa4vse4zhoreX9Li/qH8Kt+THgq8nC0gK+mmz9u1lOpdprSON6DfOkYjLseIkY9xJtO0dNsNQlJzt0Tr5LoNDFkG4GQ20M6Rp8tZzzKPB4MdnPaKzPwYGguqt2kK1gpSWYgY+uWd8nT90C6S0M6TAOOBtFvLxLWhUZsAm+snbwJdhrqrYG6R01pv+eaoROhvS4h7V0gq8m4dWlNlg7n3ckc5r7YlZIQn+/PLX1nDwCpE9CwfPjwQczpIdAOriSgDhqB+lhsB98NedEY2nxgQLcOTlUMvDvASw6GdL6O2hjSA8BCzjskH+fFNyf9OvqMvCx751D85vqqC6PjH2KjOa+eT11ScGFdPHIZnL5xUmPP10vWWaS0BOdyp0PTR4funO/T5DfyZB6Yq8CTAixLr8ZzdsUYN7aI6NNAbbeg49IZHyMGWjoN4pIvf73H+v48X7y/4+M85HrW+YLD2UNf3i3IqsUx4OQOAzwQw+L5fmkz29fTPjPn53yn35+zG+eTnh60uPFZEAsPM6GMYk0aGvohV6dNrZk0jCMAiplWRYVvicYxiH90CMOfe6SgutlwbtF1vIguHYzn79f8vI2cexfDWA11CyrA7CVMmuJbqldq5q2oaxhlhXcpjmvpxmVNDvz0hoSKXk1W/HV9YpKuZ8JPCeN7IWuN+LNsuTNNCMtFXD/YKaV5s0843Kebc0P44CLUcyq0NwkBV9eJ7ye3c9HgcfzI+d2eJuUfHub7Hy+xsTodlmQlKr1gtPWcJd0z4/7AYFvicOWx8+CUoZZVnG3Knk3z9frh41giQMvsV73S6yZ7zJ6OhkErt+utHw7Tbf+fgcs3B7dJRWvb9OdPQqDe/CljO0EX05O1v2ith3za/DV6wBfjwimtDUsipJK7X6+JtB4P825Wua0fcTA8wg8QdnCfIEDVU8nMRdHMSejNmDgQPrRIOp8yTRZVoToTDTMi7J1jw+dk3Wg0DHfvESfHw84H8cfxJCejSLSShEIj3lWbZ0juJenvrxNeTfNVFz67gAAIABJREFUdva5CapdsqdbCv5iMuDiqLf7Gb9DUL1vjxdFidTd5xj21KwLd066GNLzcVyDAqfIaO6iZqxlhY+QMbetoQko39/l3CyKnT0+dE42z0HrHn8HhhTa99gxpDnvVznv5tnOOWkSXkpblN6TFLSmdQ1Vc+fFH35f7Dsn1t6bJLXtcfP53M8eeNZGu3scBV7t7H+vStk5J2uGdP8anP9EyznBnZO3/w7npA2kP4ohrUtLSmnaS0uEA/HXSdF6joC95+R+vuPdan7Yc/R93Deht/+++eS47+7M4z625XkVOAXYsB/uV+484t3a9l46NL/57j30LO31yKD7WbsYR4ziaK0AexjfnNRdBAYdz1rk+7XMuf1Zun83Fx+9xr2JjI75zWfxScu7+8c0fgKof+ZxPu7x4jhmlpZg4fN3K5aF5GwUo5RGG8ssVVhgEAX86smIf/j0lL99MWYUB/zyYsSgH+IJmK4kpTL4CCf/lYqsUlyvCmZ5ye2qAItzFws8RN2OplSKorJribAnBJ5wfQJf3qZczgv+dL1imcut+VJqvr1L+eJqxZ+ulizyCgtEvpNLNXbvpdSk5b0hUz/yMRpWpeSrmxVJodaXwNkoohd4lKUmKyzvlznz7P6SEEBWaVa54jareL/M17+3eTB9T7DIJf96ueTN/B54R4HH6SjCgusvOc9J8u3LxxMuIL5aFHzxfsWXN6utC6yRKd8kFV9dJ53z00TyZct86HtoY3g9y/j6OqlZtu35SmtmecWqVCi9/RLZvOAL6Rxcd17E9X9K5WTSO/3O6vmqnt/9fDBLKnKleDPPmGfV1h8XwvUwW5WSm8RJtTd/R1MrHIV+ay3wIXD1WPCltKGs1M7vNwfmD4H0zWCpjSF1dSgRi6JCa8vNqtj5DhoDn7ez/S+hSuvWzwiHwdc+kP6xwdDmS/S6C6Q34KuDIX2+GQyxew7WQfUB5muRtZ8DcJ/xXUdQzfcQVO9L9mzusWjZ46ZeaVFUKNV+TiLfW6sN9oKvAyD9+VF/L0g/ZKr2vQSMHQzps0mPn58OOe7HrefkeOACwoYhfRgQhnXdW14eCAiz/QmxroTX9wG+rH0cQ9oG0q11pmo3c5cofpjwg/tz0gXSPSE2mPTuc/Js0nFOmjtvT8nAIZD+eGCxP+juYkjPDwKLaD+waM7RBwKLtcSXjqTfX8J9M4r23jcN0/7yLuU2t51Jwa5nZb0HH5g83n73fngrQJdc3vUp2UxkFB2JjEYBFvpirQDbHOs7eU9C7HwU8bzjWVLfQyLj+wL5V13P4o9k/CTx/TOPUS/gry+O+D94hwgEN6kLBn951ud/fHWNEB5/vFzwv/7mjCfj/pp1C4Qg9j2UMXieA1wIu66h8WYpRaUJPdcH7nQYk5Sud1LgexRZhed5oBVSQRzATd3GJvZ9UqmopOL9PCOVCmEFRmmwzs03kxqtNN/cJYyiAPAIfVhkTm6cFgopNd/cJnzxfkkU+hSVYxSlNlynOWeLmH/y5iyyirNxD2U0HoIodGyysYarZcEfLxecDGJy6V44/dCnMpplLulvsJCh76EMzJaSUmqmWcnTqr+13wIoSsM8k0it+etneusBV8Zys6h4M8vxBLy+S/n1xXirjvZj5wtlSDONF1he3SZ8dn7fT1YagwHSXLHMJK9nKb99frTx+e7rdP90kzLuR1tNue+DekmSa8b9mPepWUt5mpfcvCiZrSSD2DnJNX8+CjyeTnq8nqVMk4rpquJi3NuSHnrC2dVXyiAR655bD2t9y1CzLNhxc25eUqHnucboD2TMUt+vMS3Vznzoe5Ra8X6Zc9qPuVmWW59vcz72fF4cD3ekk43MqAHxD2U8DnxBWqmdWmXHfEW8nXv0I4+3M+dm3fQMbr5HZVw6fsnuGpvP+GZeMumFO2uA/VKqNfj6QKnUZk/hLqYdAX7dzuo2KYiCe7v8YRxwOgx5PUs75alxsL82sPketDUEdjfNW6n9banug5kPq8N9VL1Svcdd2fhnE/eddzlWn41j3i3yzpr1aAN8jfrd9UiLR9Rrl6OWNRw4Jwel4BvBVBdD2uxx1CKfdf+9v4Z0kyFtq8cWuLPcDwL6bc7n9VnuKhnYkqt/QM3Xo6Sb65/d/yyOo/2mal09f+Omx3hHScAaXKUlJ/vOiYGzFgdawAGPjpKBg7JEpQ/KmO/PSXvpSWNQKOiSXu5vO7UJLERH+c35KOrsCbwGTx0O+odkzH8J9835OObNLOu8bzaZ9mbNbWvMOxz0mzVmhWr1yDgkhz80v7kHbQzpprw16FCANR4ZUYdHxovjHm+mWWcLt6h51tKS4z3P0iE3506Z84E9+C4SXyH2J9jbEsM/pvETQP0LGON+gBcIilwRRB6DyCf0e/gCokCwLCTXy4LjgSsGzCrFVzcpUmtKZfj0fMDTSUwQOHbxapERha4HUxT4BJ5jPG8T5bKHwCwtWZaSojTEYcnpKOAmrUgqiTWW21XBi6Mes1xSSMPzkz7TXGKx9COfZSGJo4C7Vcl7XXI2ChG4CzD0BblSWARfX6+YZ4rTccwyl2jrJMdSWixwNS+QypBKg1aOMRzFzoU3LzVCCL66STkbKqzRFEqT5Ias0szSkvNhTCk1SSGJAp+zYUSpDQZLWmi0NWsZclNfUGqNNBrPd+/BrFIIcZ+NK7VmXhR4CG6SiqxSa3AHToacFAVCeIfnV+XWBSG1YbaSXC0LQHAx2mYVpHLzWWXR1pAU1Ra4KZUziiql4fVdzvNxvGVF3lxegfC4WaV8O024TfXabbkJNJJScpsW/PPrOdUDK/NRHKCN5e2ivd1Q6LsEwut5SoDg09M+1k7Wawh9j37scbcqUdrutCMCJ2OeZxVlZXh61N8CN1K7NS5zSaEMTyfb88o4g4fAOjn3sqi2gpVmPhY+vu8xy8udz9+AdCkNzxcllTLrfmUN8/X1XUJWaD6/XG3VKoN7iVkL7xYFgYB3817rGlaFwmK4aFkjuJdooe1O3zmpHcC11rIsZSvAbRIRgngnoGuCobJSJIOKTyaDrVrizVqgLpCOgUWuuFkVhL6/tQdR4HE2irjcA76UtihjXCJijyHYPvC170XfzEs6mPJH1OEq4+q2StXVNupjHau9vfXYAvEo8DWK/c7e0cqYzlrfxwTNjw0o2xjSrWDKaw+ajwcBb6bdNaRN2cS+euy9jtawvw6XA8maA+ekSTYJ4d4N7XtsWRRlK0jfrOtfxZJfnO3W9SP2tydrzvK4H+L77bV3vu8x6HXX9StjCGhXPHwsSN+8T9oom+2guwNYNAaFXgewmPR4fZcyzyQeuw7563N0EFhUrcCCeg1WdLCHh5J+h87RpszZdoPD/SBd8G66577xD903rGXOXWNfLXJzDo/21D9+DIg/lMiA++Rxm4x5K5Gxx0dknzt8c1aHe56lQyZIH2NyBDwexHeU0u3box/T+Amg/oWMyAtQgcEz7oEIfY/SQJ5VrConyQh8n1F8TFazOlZYyspy0o84GUZM+hHjXrAGZMaA7zkpiNIWayzjXoh1r3JWeUU/DDEGtIFFrjnqR05isxDkStPHQ3oGY2BZagJfcDyIeDvPKaUmCHxKqahUSFG5+XEv5KvrhEVekZSKOPI5rR+0URxijMUTcLcqOBlEzPMKT7iHNgq8NQC9y1xD5awq8YXA8yy9wGNVVvgeZNIB7n9+O2PUi3hWM1i+L2pDBPfPP7yb0wsDnh31kNqgjSWTCg/B7arin9/MCAPfzRuD7wl8QFsnwfzi/RLf93h65AwsJoOQOPTBE2ht+Op6BUJwMY7phf7WfHN9NCyj1AYrLHgWbSy5dAHPw3ljFYU0TFPJq7uUQeQc85p5ZTWrsuD9qkBZuwbJUhkwME8Vl4scXwhUadbz1oIycDMvuVtWZKWmH4f8p0/vLzEXuHe7Nct63sdl3Au5HbSFvmtH9H5VrAHuPHMsZOh7BL4gk4pXs3QLAEOw/j1hKEjnFZ7v836ebwHMShmSXHOTVuSV4WQY8dtnk3XQVylDXhpWpebmJiMOPC7GvfXLvAmGCqm4LUq+vl3xN+HR+kUWBR6n44g/3Vi0tryappwMo60XXeQ7KfZtUjAIg7Wh2CaLG4aCfFkRBAHTZBskg8uSL/MKreE2KbZA/iZIR9hdgGuclDzJNFWV8+XNin/onWyB9EIp5plkkUtORkv+Y3C8BrmbIH2VaSaDeItJH8YBp6OIRVkyigMqrcgqvb3GGnyVUlPYdin1KldUoYa8nUm3gOeJVtfu5kV+k1Wc9doDtoYpHwQhn509YKEPgK9K37t6KyP3B80d2fhJP+DlrWYQ+1wtCl4cD7a+44b5ul0WDHtBazzxKPDVVRe3sQZrd9cgzf7WW5vBUpsz6CGGFO6Doa567Mjfz5A2gf3oALiKPH8vuJrliifDdgfXj3EqboztIt/DtO2xNvhCONliC0hv6vpvVnpd1//wHH1y3Of1XdrZngzYqzZo5rtM1e7PiY/uWMO+c6IeBdIfByxkyzl5DEPaOOSPo476yEcAi0c56HewgxwA6cbYveyf+yH2MqTNfdPF/h33Q77Rmn60/745yLRnGrOnbOJDFRkHlTvmMPu3jyHdVO4MO1v9Nc+SwqPYbQ9Wr/GQO3xXy6VmvssE6TGqlMcaXbYZWR66kw/t0Y9p/ARQ/wJG6Ps8m/TXWfxZJrkYx4zCgLnWJLnim+uEojJ8djYAC4EvqJSrGz3ph3x6OkQbSz/0+cefnfD2LkNayygOiUKPMHABgOdZLqcpWrvaVnqOwc0rTeBBPwq4nmcIBNNM8mTgMepFKGsBy/Eg4mZZkJWaN7OMySCiH7kHxgo4HkS8n+cY42pMj+KYslK8naVM+jEX44i7pGTYCzHW/X1gKbWi5weMYp/LeY4QLng/HfSYDEN6kWAcx4x6Pl9epQCkhSaXiqtlhdQCpQyDeLsvVKUMb2cFp8MYpYwLEnEgzOCRlJJvp/l6/mwcMxmE9BoAqiyvphkng5hKan5+NnBMsTGEno80lrfz3Lni5RW/ejLClXFYQuFhLCSF5OvbhEHgsywVk0HI+chJrj0Br24TvrlNGEQBhdKcjSIqqXg9ywl9j+tlziwrOR30kEZzOoiIAkEY+FTKUijD719POR32Gfd8BpFHJiWh8FgUFUJq/unNjHEv4mIcM+kFlNqgMKwKi6wDji635rb52Upyu6wAwc2xY3kDTxCH/hrANgD321nOxSRj0nOsvuexNf9mnq/7//qeM9SRyjDNK2LP53pVbYGb0PcY9X1OVIDnKbTVfDtNXa1a/UI8HobkUjo2u1S8mbmevMM4WIP0eSK5SxRJviD0BZ9MBoBboycESa55fZchreF8FPObi/F6XuoHUuxpwm+e3jtPhr4zDMuVZoBAPAgqA09grMUI6Mce58PtWpYG4BZLSRA4pcImwJXKOWLfJAUCwcUs25KSV8qwSBXLQrHMJSfDhEkv5NdPx1sgPSklN0nO5+99CjVcM+lR4PHJSZ+Xdwk3ScEil4x7Eb++uF9jHPiUWvHuuuKoRabcMOmrXNELfG5W5RaT3oDwrNJ4nuWTk+EOyyyNIcs06GqnLVVzzhxTfm8M0swHvtj7og+8ezv+UUtrDcds7c/GCyArDYGnWHWAdGlcSUEmdccaHgG+OhxoQ39/SwG5Aa7IdoHJZslAVu3K6ZtgaihCpN51FX+M1HvdVuqAY3WX83kDrgaRRer2oNYCPa9d0tbssXgEy9wF0vedE3CJ4K567HXpSVJxNIi4TXeTVQKXqAmDD1MbrIFHrjhvAenNGjwsR23nRO8/J4dAelMyMBShe2a/4zmJAo9nk5g3026GdJP92wfSu4BFs8Y46HDQr89R3NVH3Tz+HH2ojPnQfeO8QQy+UCR0+yvsS/ZYIA7a1/h9KDIemwz6EPbPlU3Uyp1BR6s/3LMU+O3PY3Mf+GK/mmDfs3TIzfnRqpQDMuau7/CQauVsFPF6lhKFojWR8WMZPwHUv4DxfNLnb56OuV7lTPoR58MeCBj1Qi6XOUkh+cO7JatM8b/99TnPxgN+djLgX98uwbpWMb/7+YkLagOP55M+vcgnMBapDE8nMU+PelTKcjoMeDcrOB6V+AiC0GNVKKJAIoB/fHbE26kDRnlpMAPHt1priQKPQeRxtSyQxrLIJb3QIw4d69rMT9MKZS1Xi5LwxON4GDGKQ8LA/btA4HtQSmcu0QTmvic4H8d8fpmQV5q8NJwMSkZ9j0JpjmIHaB8ypLdJgdKWNPb49HS7zu3h/GehA/J5pRFYpmmJxaK0ZRUJTsfRGoD6eBTKME0LjIGpsEwG4doICuGMhkrtamVdnzrHmh73YpJKIbXmy+sVs1wyjgPKSiGsY59UJulHIW/nGbk0HA9ijHHzw36InWZYLJcLB9zyyhJ6Tio3rY2L0lIxCIM1SM9Kn+NhxPHQ9coFizLwfuFMBSqpXYAceniArvfoi8sFQRDw9MhJjjfbDTXzfs0ya+vmS6NBeFssc+Matwlws1JRSFW3OHL/XKaKXCuMdGtYZJJ5XnEyiMgrhbUwiSOSypl4vZvnHPVCvLpNUi9wktN5WpEVA97MUrS1THohlTGcDiKmWUngeSxzxeu7nON+wtkoxvcEZ8OIKPIIPQsICqn5+i7hKHYgWtV7oK0BLIVSfHG9pBf49CMfZRxIXxUGg6sr3pSKS23wPIEvPBKpKLSr394E+QacGZhVvF9k/IdPJmsmHdgCuM17bnN+2PeJcg8PJw/cZOKttUyGAdMsp9KaRaHWCbBGjq4MTJcVWWG4nOecbeoOuX8mj/oBHmKn710DEK1xEuWHMuXQd/Xvq1KijW1lmfuxRyklvudjzHZAJrUD2VmlSEpF3iKDTnLNIleoVJK/UFvBgtwAX7lsr2UOfY9cSgopyUq1U2v87Cjm7TRlmlZUUu9IpaPAd67cQuAHu0Fzs4ZSadd78AGIbkB6FCjylqbq9+CrmwEVQKE0x7SbhjTg6qi3C0yakoG0VAjg52fZFnBogqm01Hi+4d083wEWp3UwFAbtwVBjmhYfME3bB77G/YDAE60tjx7DDjb9s1VLQNqA9J7veoG3fb7mnGhrduLqYRwwCmFRVN3g66jH+0XGKA72MuEmthSyG1hEQTcI3weuNhNmE/afk7ZExyGQ3kh801Ljqf3nJO4Imr3afC/0D4P0tjrb9XyuCFqARZOQ8oHxB4B0qe7PkTS7LPJmMke1gPQmKXizshhLK5P+vL5vuti/5r7pBz5x2NFCxHS3E2rW2A8tiWo/R+s1tgDEQ4qMTaa9UIeY9g9j/x6jyFjljnwpZPcao1CRVd13bmei4hF38se0+gP2tlSCw6oVxP7E6Y9l/ARQ/wLGySjiv//jJ3x+tWQUhRwPQyLfSRW+uF4AcLMqCAJBVmmOhxG/PB3y+9dzBrHP61nKbVLwdFwbAgmIggBhNFmpawlwiO/5HPcD/vbZmOtlRqFBGAh8n0HsU1bG9XcyGqUNAtDGYKx7UJo2Lz4CT1hmmeRkEOILgbUWv5b/+kLQjzwq7f6fe7mFuJ7yYp0VsjhwtKk8CD3PZQ6lJQgE2lpKZRmEYs3kPRzGOgY2NuHOnLWuNqnUilC5eW0snnAZb6UNUhmU1RSVT17pOqh1RlHLomLihWRSEfs+l7NsywiKsUVJw7JQnAxDrlcFi1QihGCZu4v+dlVwtaqY9EMKaejFLolwtSgoKsmNcTWipbRYYXk6jpHWgHAS5NNBTKErlLbEoeCX50MgJxQ+87xkFIekUq5B+G+ejWureUVYBzSbIP1XvfE2S6zhZc0iS6XdS9wThJ5wMmdl7uel5mS0nYkrKsPlomDSj1gWzlTJgTuXRDDWkpWSq4WrZexHLlCMfI/MaDCG94uM27Ti/bxEakmlDIHnMU8lnNo1i3zUiwk896JfFDGvpxlXyxzPcxIwawWDyOPJUczXtwmFVtwlmuN+xF1a4tUtPQLfgYm7tCT2fWap4mqRY8Z9jvoBAsGoFzjJnIDblUTrFRdHffqhxzD2KbVmURZrqfiru5Tjfkjge5Rau1ri0mJqgPvqLiGOfHqBj6znG4Cb1nWgb2YVUeBxvSzqRIhPpjRZJZHK8Pls6QJNqQg9QRiIus4V3s0zLuc5vueYw4tRj7RSlDInKyTvFxm/OBsCju0+G0bOFEW4nsXVAxCtanD1flYhreHTs4LfPjNsssiLVPFmViCt4dmk/51ZZoD3SUmAe04e1jIP+z7XqxxtnCvlTvsLYVFoSmNRqj2gXOaOpR33Qv6XXz1ZB41NwHi50LycZih9w+9+NuE3T4+2AiKD5XKeE/qC86PelpQ69J0r97fzjEEQ8O4032GRJ8MAtdIuoGIXHFjhkki01ME2wZS1TpHSJZN2ILxdJi2ApFKETdH9xmiAh7V1skltJwEaqXehJWeDe9ndtvQSVrlG6ZKgw3BrnkriUBHtCQi7wFezhlXlejN2raGLHWz22AqLtf7OXMMOekKQ1izy5h5vAgtt2oHFOBb1v7eDr0HsE3geaS4JOqSX4777nYPQ7wRP/dBfJzm216CZpxLfE5RqT6LjAIAtlObItAPYQyD90DmBw2qDeSopwvv7o20NUQf7t54/BNK14oj9ILwNpMPGOeqINZq6fYRr0ffwLlAGbpclw37A+2XOp2fDrXOC2M/+rddQ7TcxWmaa4FCyJ9z9njfXWNOcW2MTpLcle6qN+TYp+CaIbzNI3ExkdLF/8QFFRrPGOPDotzxLzRpdCrg9qXjoWbpPVHz3hJfaAvndrt9dTPsmiO8HXQaFLpHR25fI+BGMnwDqX8i4mPQ4ruvcmsP0d59M+P3rGfNIYoyrq3t9l/KfPz3j6XGPSrngdp4pZokDMn18Qs9dYnerCqkVYByrGgYI4GenA4ZhSBhaotDjZBgw6gWMYkEvCjjux8RBitSOaYT7mole5OP5zrnVgT/3wJXK4HvWgR7h6s2sERSlIQ0U72YZZ6MevdBfM5gerjfrUX9DUqYNSSEpjMGvDEWpuZznZIXiLqkY1j9rrMVYx3z69T1qWpJExkLoC3xPuJYtxr00+5FPLh0g93yBNhD48H6WMa37d2aFJpw4BjEtFOOJzzSXW0ZQnuf61EqjMSYkKTUIyyD2UcZJsGe5otKaQnlo6wKsvLTk0nCTlJyN+muQHfkB2hhmS+mMhGoA2swHnnsxprkmqxlKbc0ahMcmXAfCgXABn7XbID7wxZZM2dp7AJvEgnH/aAvAYmCeV1jrWORe5M6nJwTUtUCl1o5FxqKV2XJ79rFU0pBWitOhz92qYNNsKwh8bpKCrLIc9dzvHvUCCun66S5yxbfTnF7okw8tgWfR1rJMnKnWLCs5GcRcLQuSXHE6inhy1HMvehyjbaxdzz8Zx5yOY9wr2ENhqWoWvJl/ftpf9/QNAxfYS2MoZB3o4+/UKi/zEhCMYt8xzBsA9m5ZsTqRWOHYWg9Xy7WqyhrgltysCid/j0JWhXNQ9oVPJd3v+sPlnGkmOemHrAoJVnAx7jHPEjSGVzcJqTRcTHr4OJmzNxVo3N5npeKbOyf1FV4DnkKmaUFf+Fhrt1hkaewOi/zNXeLaVNUBZKk1hVZ4njPGeHmXMKrnS6UPssyHapmNseTKEPsukfJwvh/5CAv9MOCqrnVu3JQbhvY2daUA/3q5ZDKI+Iefn2yxyJfTgneLkqIyxKG3FRBJfe/K7Xvw9KjX6cqdB5ZXd9uu3E1AmUmFoANc9ZyEO/aCVkOxUmvukpLI9/l2mu3IpO9WFatcEoRiVyZdB0NVZbmWBV/dJvx973gLgD4/6rnkAJq7RG4lARqp9/tFTlpqbhZtNV3CBXzW/VfbUNaA8vBatJdNwBf4gqKDRZbG7XFVFjsy6cewgyeDCIFlHHcH7auiIhLhjhT9UcCCwyDdsr/1lgCKSjFsCarXwKJWVrUFnMoalLJEwS542g6qd4FDIxvskug+BqQ35yQrTes5ieo17AualTUUykPsOSeh3602eAxIjx+hRpjY/eeojUUexgFH/YA385STfsj0gZQ7CjzOhhH/og2RMtwl5S540daZIEaaoNx3jvaD9NBvP2dBcx+pWsm1Z42TfrucXVmDNXad8H84nEeGoaz8nb9/E8QbdkF8M1a5RhvnPbIL8OxBRYZ7lnRrsqdZY5cio1mjVi7h1DaaPWgjRTZBetUC0svvISF2SMIb1XtQVpreTwD1p/F9jMagpRn/9RdnfDtLkWhu5yWh7/HltetLGfqujiyvLFlR8c1tghCC372YMIgDfn7a5+1dCsLj1W3BP35qGPfcoX026fNk0kNaKErJz456PD0dchQHnI9jfnHW55ubEA0MQ38tSfWFIPRc24eotilfFRWecPWQ2rg+qaM4JBQeUliSosJg+Nnp0NX+1dlfo8ELnLHAZptOZSy+JxhG7nLzfJDaggdS37c3yUsDnuUuLTgehOvawmZ4wvW4qqQzfmoS77fLgsj3OBvFXM4Lnk5itGENQG+zimlaoo1hVbm2NqHnGC9jYZlrhHAZqqTQtRGUJvAtytp1He4iqwDL1argfOSYbSeT9rfqbOdZyTgK1x2JfU9wPIwodYKyhkUGF2ON9+B+2ZTgPhxKuxrSQmmMhGjjfmxklpsA1hjnetywzA8BbOB7jPs+Uis8fG6WxRYAjTzQyrIsJCejkNkDt+cw9JnnkrukQltLkt+bbV0uCpQ23CaOHXLSbTjq+VzOLVEgmOUlwyggKeWaRf7ZaX/DrdlQaY2y3howZ5Xa2oPN+cocnt9k0i3upWFqp87Y79OPgi0Q7yN4Oy+4W0mOhyHPT9x3bozFCOFadCjD19crwsCn5wMW+oFPJjVKat7epXwzy4j8wMmcsVRGMs0LFlmfflCQa0PkOXbTCkuSG25XBV9e+TwZ94hDr5YWeQ44AEVukJ6ct9u/AAAgAElEQVTh/aqiH7v68FHPx2IxxjLLqnYW2bLFIs9SxZtpyvGwx+nQJUI2DcfuVpLrYYEyPY76wRrAbrLMf3g7XxuOAdu1zJNybealarmvMhZZGqRnuUldQLcpcw49QakNea64WoZbhmKh7/H8qM+7WYZUlrR0ibLPzoYM4wBfOBY5k5pVXpBVirNRvOXK7ZIUztU7wLl2bwYcUm+7cj8d93bmC2UoCoMXeLybZ3UdsAsmgjrZt2ko9rA+URtLWmoyobmcZaTPjrYltLZJFEBeyW3myro7c5a5Z+ebmxW/ejLaCpovJj2O5y5TX1a7jtHgWB0BnY7SJwN3x0YdgZBfO8lrs8sYNGuoKouxu0Gt1K7eOik1oS9aZdINI9H2+zfZP2Xa2b+Lus3Z2SDaYf8eAyyaNWSl7awba6Tc+2SFh9i/TGnGLezf/R4Lgocvi43Ptw9YfDT7xwH2L9jP/jVriIL9azCKtQKmbb4LWDwGpD9W6t12jqLA4+KoxxdX7o5ftMiEA98pyJQG39sF4ULUIF1CL9zdg81zlHeco0cx6R312odAOrjvyMVy7Xt0MoiYZ2Wt6tlOuG2C+EkLiG+GsoaiEsQtR/X7UmTkSjHuAKi+J/AFneewGV0muw1IL1pAepNsmmclHv7OHm0+a4NeN9P+MQmxH8v4ccLq/5+Mk1HEf//7n/HfPj3H9z2wzlDnNikAl7kulWaWVnx9k/DPb+brno1nw4ikkkhjeDvPWORy/Xsn/YjzcY95UrAsJKm0/Oy4z189GTHpR/zXX57zs5MBk16Msu5hPR3GXIx79KOAXz8Z8nwcE3iCUkGhNeejHmfDGIGgF/mEoZM/rJRmlikMgqRy7WCSQlJZy7IomWcFZcPS4hx4wYG5SlnyQpOWFZW06Fp6vCoq8HDuwdLJB7W2ZJUmq2sd+4FPViqSsuI2qVjmhiiAeaFQxnDcjxCepZAGZe6lzHeJIvA9nkx6xJ6rKwt8r36pWHRN02aVpDKGVSmplAOvTZ3uUS9gmSuKyjGkd0m+rsNq6myNgbzSTBPFbVa6/mT1CD0HlJLC1d0t82prvhnGWpRxIKEZzYXZBK3lgwRAkyRowFmSK5JKAXbNMjfgLPId6C+0ph96FNLt4SYArZTG8+9ZZGtgnt0DUGOdLKhhkStloAbxoe+C1lnmvl9jLaVyTNP52J0lgKx04L9hgbV2e9QEgpUx63rURa64Tct1DWmzB1vzSXFwPq8BbCYVy0yRlmpLlgN2DeKtcefI1DL3rNJr5YEnBNSJkihw0l/Pg9ukxPNg2AsopEYZy/u6ncvTScxxP+BkGDGOQ6QyXC8LblZO+jwvCkLPOTquygqL5WaVU1Z6DaKbM7FMFauq4i6rKKTialnw9fWKd7NszbQLIyi0JpeOBf/mesW7abZOVORSuu9OGr66SfjjuwX/9na5fukpbSi0pdKad/Ocb65XvJ2m6zPXzC9zyatZxiyVvLpNySu9rmUurSGTmtfTjN+/nvHt0iV/0lw7prlQLLOKVSH5v19N+fz9km/vMp6OYyb9iFxq5oXk3TTlf3x1zR8vl7xb5Lw46XPUjyiUZJ6W3Kwqfv96yh8vl7y8S6C+c6SxSKWptObr6xVfXq14eZu45Bgu0SCt3WpbVdWS4s01PJzfBLCX05z3SwfCN+cbw7B5uuuYHfrO8MRog9aGuwdBb+g7V/FlXnG7KvnD2wWVvJ8fxAHj2Of9ImOalXx9m5DVibfNkeSaN3c5n1+nfHWTrNcG9yztHy+XfP5+uTPfyJxf3iVcLUrezfKd+XEvoFIageBmVW7NgwOus6ximUsuF8XWvMUFfNIocqVAiK2gspG/JqXm/bJoXd/5KKIXBGtG4uHn++Skj4e3Zv8eBp6B7/pz+8KjiyXWxnKdFMzT3TVAfU6U+74fjibonmVVKwB+DLA4GUQc90NOBt3gb60kevDnB5EDDquywhesgcP97xfrFm6yA6Tfr6HYu4YuiW6zhqNeyNmoo+0U3aCgGeYhctwY7s53vd8fjkPnyAq79xwB9COf83HMsE5gPlzD/clp/4zN2uyhNXSco2a+UnrvOZtninnVbRS1D6Sf1O/uqDa+29yDQRxwOojWscLDc9SAeFH7KrSdk2aPuvbA2pqhVfYjn6X953DS7z6H+56lzYRdA9I392j4iD069KxBk8g4dJ+UnX/+xzB+Aqh/4eNi0uO/fHZKLxD4vseidK1BwL00V6XkNin5l7dLPr9cssir9SVgACkds7Uq7gOb42HEpyd9cukkbe8WGVl1bzByPu7xu0+OGcUeR8MYzxMMwoBPTvqcjWL+7ufH/NXFmH7kczGOCX3nivdsEnM6jPnNkzF/fTFhFAeMQx8PCIXgZBDUNYg+Z8OQtNTcpYo/XSVI5UxlQs/jZBBzFIdo42rzKmUJfDjqOwfEQLg6nVUhmZeaZaG4XpUsi4o/XadgwfNgkVZUypLkFau8pJCGWSLXRi+ztOKb25S3d6kz/tGOkTnqhRjtjGZmq5KiBhsGdzGNe6EDHhZuUukMgJo6XE9wXAeMqdRMU0le6S2QuFlna61FKY1S25dMwzLbWn76cL5ShrTQFJUL5BsAu2mmk5eGUmqWlQPgmzLnUmsK5Rxu01KhrWs1ZNiQOWvL9aJglhTcpgqpHNhd5PfthJLC9cl8PS3W7WkEdg1AwXK9KtZ7aC1rEF9Ig+fBLCsplLugm1VuAVCtt0B4MxqpeFlplnlVG1Y5ALs5Xz2YVy3zi435JngptSYpFYXU3CQ506RYBy/LXK4Z1kxqpnnJNCmZ5i4hcjnN8IVgGAWkhWJRVnx+teJ2VXG7KpjVRh/jOKCoNO+WBS9vc25WOS9vMjJpOB24mmVtDJfLnPeLFK0NsecktCeDaCvRcZMWaG3o+cH6DJVasyzcGZylJZXSjiWuWebZSjItKmaJ5DbJ7+drFlkZVwOeSsNdWtALfZ4e9xj1A5Q2W3s4TUtXxyqcjKlhWEtpkPUe3tYy5sa52fdcAsgYwyqXTNOSeVbxPjW8neUM+z69yGXJ75KSf30z4/U05d0i52qZUSiD1SC1ZpEUvJzmvJ0VvJ3nvJk5EBwG3rq1hHN0Lngzy3k7zdafIawZwkoZvrxJeXmX8WaakW8EqbY+My9vEv60BrC7QOvVbcoXV8v1/CaAbUD85Tzn7SxbM/mpdD1zNx2zK+XKPQJPUBqNNBajtwHwIAo4iupnSQiuFjm3abH+PFHgcTKMAZcMukkqFnm18yxlUnGTFKwK6VjaUrXO3yW785sy55uk5NVd8gDcOKl2Jl3iplFxPNy3ddutFnMVv5bjRb7H+WhbEtgwEoWSZKXh1d0uAG0YiaSSvF/mO+uDw7V/J4OIo0HQGbQ2d5TnCWgJ7qG9vUgzXNCt9gOLOoG2j/mC3fYhzee31lKq3T3aBA6mAzhssn/7wFHRBRzU/qD5niV2hl2dAHUPeGtGW7kPHAYW436ANAqpd4HF9jmqWs/RplR73xoOwYWDILxj/+9//2GQ3mbAcwikD2q1QT/yuTi6Vxs04/s6R9AF4TfmDz1Lcj9In2Vy7zkslEu4dz1LnnAlZB8CQB+7R9q0P0vrPeicOZzI+DGMnyS+P4Lh+yCNIJcl1gpWecn56IjzUcyfrhZIC29mmcvWJyVAHdh4lNqSZCVfXK3wheC3zydEgcfTSc8xfxiW+T3jI3AP2NOjmCSXWF/gYTkZhnx2NkRby4vJAGWcDEgEHoGCX12M+PtPjpHG4gtBWlW8miZEgU8YwPPjpnbL49OzIWkpiXyfk2FEWmgC3+NiHNdy0oBRzye71mSV69dVSM3xWUgqFUEgGEUBldRoA7crFzj//HTAqlRIpWtjEM3bRU4cCp6O+6SlIi0lz457rPKcsjSsCkUZwrEMMTUreDqK+adMURrLt4uCrFT87GwAOIb0ZODzlbIYa0hSyUnsWLXmvhQ4qZVUmqKUzHo+J4NtV8ukkCRKoTJDFAo84RyOCe/nM61RhWYQeQjrpL+2np9nJdJYrFVkhXNTXjOgUq1Z5rySRMrVY+i+O0vv5znL3DFvSamc5b5yrnKjic9tLlnkFaVyQen5OOZymtGPfZR2UsCmnVCpNJfLnKEfcjGJ6wy5A6DvZs28c30+GbgeuFHobbk1Z4UhEDmn44hhfH8lNeAHa7ak3Jvz4GRRmyC++R7WAWPttqyUvzPvCRe456Xa2kNZRzj9yKdUFqMtUhoy6dxCG7Msay2LVHE+tGRKI1cVF0cxq7qd0E3ioWqJ53QlkUYThz6eB0e9gDdTJ2NPC0kcCI6HA8YDj2EQ8GQcY+2yzvIKcmm4Syo84VPUZlbUssnYwzFsG/OjXrDeA98TKGPX89JohnFAqZ0qIfT83fnIZ7aSlNrgaw+tDbPM9W09Hrrs8qbhWFFppklF6AWklWLSD/E9waAXUFRuPyt1v4fztGIyCBmGPotUcrss+Je3S+JQkEjLMquY9Nx3lkunFPnjVQICTmojlk/P+mt2fZ4rjgvH1CttiUKXIGiYcF0K8rFCJnrt2v2r3phhz5lmeZ4PVux1Ba+U4YvrhNNhzDj2122rNuc/v15xOnDzR4NtjZo2lrtVTi4NJ4NgzQ5qnFFSUep1X+Un49ixSYOQXhC4llaLjK+vV4z7LgH09KjPcS1T1loxy0q+vFryfDJA1GfD9wW+D0lRobTg1W3Cp6ejLUdmbSyrSlJIsyNj3pwPK70zX9WGX43M+XwU78icDbBMJXem5OlRtGWW1YxlJrHCuZb/x5/tTJOUklJZ3kyzuubKBY6DKOAoDviXNwW9MOCPl0t+fTFe1yI3jMTvpUsUNm7Rm6NhiX2/oh+2y/K0MSwyRTRq77Xq1w7jTiUiWn/mEPCwe36mkegGsr3GdF87oGaP/u1dSRyEfHm12tojOCw/vf+Mli4W+X4V3WvQHRLdQyzx5m/v2qOPkZ8OIgcs3i9yJn1/bw1pqLz9LHFHjeh6DQcOQpd76/0efBxIt3VCep/8VLTITx9Tk35/juwHnaPmT/yQe7RP7r75LDXlQA+fpeacHHeck4ujHv92uXwESN+/R4cTGQf26EcMTuEnBvVHMSI/4Nmkx0k/YhQHfH2bM4gCPj0bEPkBR7GP1Y7F+MO7FUnhsjVB4FNpzdWi5J9fTfk/v7hZy4NdDauglKCU3jIocUXYsTNBsqx/X7hxSZ2PYk5HEaMoZFQDsH6t5//N0zF/++KYST9k0AuJA59nkx6/ejLibBTz335xzn/59IRe5JNVitJojvshL477TPoRf3U+5m+eTRj0fUZxQC/yOOpHPD3qMwwDfvvsiF88GTKIQ3wrwINB5DGKIyLf5+Kox2QQE/keZeWYxnEU8PxkwJOjPqfDCKldUDtLK9ccva59cY2eB1hrkEqTZYppXlEql5WOAo+nx33CwEdKSyEV75Y5SaHWF6Lvi9rAyVDV2bqbtETXcsEmo+XhjJZmqWSWl1S15LKpwx3UgdKqUCyKCm3dXK60M4AJnSvk5dI5B0vlLverZUEgPHqBxyytWJZwuSyYpYWT6GYKzxOMw4BlLnkzz7meFeRSYQwsUsVRP2LcDymV5d0s590iQypnxhQFHpNeQFZppLJMU8W8LNHWrlnkJ+MYpZ30dZEp5klJIfV6jxoZs9SGXGpWhWSWyPUegQuKTZ3xX6bVzrxf9xJdlYpZWrEq1M48uLq5y1XJvKigCV5qs6w49FiViqxyMlKp3EvhduFqlQNPMF0VlEqzLDXLtCIMYF7I2gFaM8tK3s0zlqkDR8ZAKi2TfkDk+5TG8HqZ8/JmtbVH5+MYrKCUhqtlyd2yYpFLitKy2HjmjLGUUqK1c0beZIGbNTo5kF3P6wfzTU+5Zr5hibfm5e58Y7ySKZcI6Qcep+Nw7Sbaj3wGcUCpNKqWyWfSMRCztOLJUcwo8ClKRVUZNM5wLPQhrbQDoMJl/BsTqmXu6iWXheZ83GMYuSAi05b3S2eYViqFNtty+KSUTNOCQqqtPbhnkdUOk26xrDLJMnc9Y7W+l5JvZp4bOX2lDLdJwfWy4P0yXysXtuZXbv5yme0GsMryxysnk/6XtwsEMBmEBJ7AA66WOV/fJqxyxZfXK8dgCA+MS5DM04rPr1d8e5fw1XXKlzcrAt+5VZZagxVcLkv+6fWMb64TXt2lhL6H73nMUifV/vIm5f/59o5vrhNe3iWoDRmzNk6mXClXjrHJDBhjqR4pc95aszZczUrezgtukor3q7JVhuueeSjVtsNrY4a1zJzR1x/eLphn9yxwGHiMeyHLXDHPKr65Xe2wxBbLoqh4t8x4v9hlIKU2XCc5b2YZl/Pd+axS3KxKblYlr+7SVhba9wSTfsgoDng+2Ta62vwch8Y+8OV7orOOt6khvU7KVoa0YdIDH2Z593dw6DMemj8UFHcF1Q2zdZdI3s6z1s/3MdLKBlgU0hAeYrbMAWDRwWzdS2j3SysPAYuD8xw+J4f2yLB/j9rYv2aY2r/A3fXtazwIrg7MHwSgj9ijLpB+/zu6/nL3LN10PEvfF0v80QD0e0hk/CWPnwDqj2D8/HTAL06HjPoBv3424skoBgH/+19f8I+fnfLs2LEIeII/XS25XGQMooCn4x6rQpJIyZ9uUr68XnK9uJd/uXoaVxv29XXCl9errQet0Ia8dD0ItbbbD7OFtDDM04LkwQXmAExI4AUYY8AK+mFAUBuP/ObpmH/47IzfvTjixcmAXz0Z8YsnIz47G3LUD/nkpM+npwNOes6oKfYDPjsdcNR3LOnfPJ/wm4sxp6MQPxAc9yP+7vkxn57G/P0nR/zuk2NeHPcY90JOhiHHo5CTUUQvcK17/sOLYz47HXI8cFKVfhRw0o+4GPd4MooZRIHrU6otSSVZZQprLafD0MlbwrqPqG8ptJMmXi1LsKKWC3pM+rErULeCsq4jMNbiCQdgA18Q+a6NS1Zp3i9K1MZ8FHgEgSCtNItc825eUErXHiQpJJHnOYl3rpAGXs8zbpOccc+nUK5FT8/3KJVjL+7SklWha0moqY2jLMYYKqlZSrmW6PZqBrQsXW1qKl2tg62vO98TPNmoI8lK7f6eGlBC3avMF1RKkxaSeS5Z5WodDFP/HlmzxcvS9dGsWuZXecU0lySVpNJ2fan7niAOA5TWpOoexEvjwI7vuXOXl5qiMlwvSpJKOQC6cgB0HIUUUqGBpHAAJwpgXrqEQ8/3yJVmllWsUkley35WheGoHxJHzv12mTsJrdGuXlZgXU9YBEII0sKwkk29cr1HnvuORc1u5kYzSyqySq5far4nCANX/yuVYZnvgvAwcN+zA3ft85Uy5IVcz28+y6HvTMCWRenMvWoQn0vXvzYInFz+tpB8dZ0yTSWzXHKzLLg4itf1MkoalrlkmVZEgTvXx72Ao0EEQrCSknlSrUF8A0DHcUSpDO9XJe/uUox27oXKWkLv/2XvvZ4kya40v9+9rt1DR6TOyhJdoiUaYhbgzOwoI21p3Pd522f+Z3ymGR9ou7PGHe4oAANMA41Gy9IqdUSGcu33Oh+uZ1RlZVYDy+GaDYa4j3UqIsM93D3ud84nJL5roWvNWZyzSAwVtrqikZE2f//NRsa30eXzUhvqLCav92AeG5rzq8fcapKf5RXj5SsAfK5PPacx50XF0TwlL6omtso0qc5pzJVSjJeZYXR0PSTG5XEUutjSTO9nScnLs5Tn05hnY0NDXmt7CClMM6/RIz84jjlLMk5mGdQ1oWejlblDTxeGTv7kLObFWUypNC3PTNstKTmZ5XyxP+fROF7RnMFsdsoGZH/ydMxPH0+4fzy/cK7Pz8c3BzO+2J9f0Om+vuK85HhmGnel0pzGqaFvpwXHs4vUwXOd7TI3Tab7h0uS135TQtem7VpMkpws1zydXAaINTVJWTJLCibxZXOatFDMk4pFYuKu3nx9WihmScUyqzia55fq87Tk2TjhyWnMp89mfLE/XW1aX99Equb8vW3TXuma9ArtH7xqFiW5ugTOXqfoZldQdM81pOPY6Of3z+JLwMJ3JZZlIqrUFdTFV+fy29dvnAL/FlPit61cKQ7nCUfTgueT5BLF9tuolcFvAFf/XwDQ3/YYFllFVr0dfvxmYPHb0Hwv/5ttid8epEvx1nOktYmPO5pf3ax5epLwZGKaZG9rdPymK+m/J0h/9R5X//vqXisu32vn99I0NV4Wx1dQub8NgL7+N7+Nam3+7z8TgH5LXem6yT7/3QWpv6f4/g6s9a7Pf/jDG/zd/RM6vkM/cgldm17o8pc/uMZ/jWz+92WBa1s8P0s4XeR8tNtnp+/xDw9MFMp4kSEQPJ8mK21TJ3Q4nFWcJSWfvTyj0jWDyGWzaxxIhRSoypiHnC4M1fXcxKjUurkTJarULN7otjuWRDQ/pi7iAqXHZL3ZVJWhXtqyyauyJY6SXB9GWBJujdqYe6tmdxCyN4godU3LswkcyW43MtRBWbO3FnF3o0OpayLXJnQt7h/GZA3d94PtDrfWO9iWJPJs7m61+eZowTwvsDAPm45v4zs2ncDhznqLF2cJx4ucjmfhWhaB4zBqewSuxbsbLZ6PY15OE9q+gy0loW+z3jJGUu9vtTmZJTzQMZ3AaKdagcNG26fju3x/b0BamA3RIHLQQNezV/Xv7g4oK83JNGPUcsmUQgrJjVFEVpb0Wy7LzOeRXDaOwiWeJVnvBOiZcX1L87pxOYU011gNFTNwJTuRx/E0N3mteUVa6cYpTiKBW+shj4/N9KUsKuaYB51jmR9315IrkF4VmriomeUlu+LVw1+KBsAWFZZtLNURYlUHcwllpZlwWVKg9cVHel1DUipEUqBruLluNg7nANSTgnlaUWmYW5VxaHUdDmcpaWGs9GdpQehZpFXJeFGw2fGZZQqlNXUT6n0kDHDot8yP8zJTKzpxUdYcL42Os9tySApzTQ0jm7LUZGWN1gVaa3otl7w0E27XMtPBrChRSjBJFC3PoRu4RI09YV0bvVKSaRxb4NsFy9zHc8zfVromySvirOLQFnS0ohe6hK45S0WljdFVVnFsCzpaX6onmWKZlxzH0K9hveOTFopFVhDnJRrNOC4QQpIUGf2WB75k/yzFkZKWa/O0iI0BVprzchJzY63FojAuiLY0rrunaUHeTP+zwujp7m6ECASFVkyWNRJB5Bj3Z1sKXGmuI8sSFIUmVobKq7R+Q3csKMuKsm4md+pibzgvNXGu8ByBlGLV6Pg2urx5stRM46Jp9NRM0wKBoOO7qw3gOQAuq5pxbKKE+qFHWb26Dn3PYrIsOU0KLCnohD62g6ExRw6BZc5nmivmacl+O6ft21gSNns+rpQcLFMcW+JPc2ZxyVrX4/7JkttrkckazQuy0mI9N3m1ReUwjgt6kTHGOfZspDQxRseLlErVOParKAUhzHQyLkvGS4kUEs8RBO6r6C9Ra15MYpK8ZK0dcjCVXB8amvN5tFdaqCYXWbNILcKGhl3pGq0Ui7Tib++f4FsWa22PduDQCzwsuaQszIQpyY1jdOQ5DQCVzNIKS0oO50ZH22tyHB1LMmr71DXkWjFeamOglVcXQFaNcW4XVwDESmmmWUGSV9gYmvPt9Vd5t6U25ntJqagVPDxaXKgDlKXiLC2QteRgdtltuag0z8cJCPjk6Rmjln8xqgXzLDrTJY9PlpecO9u+zePjBZUS/NOTyYXXh46h6H76PMW1LD7fn16g6Dq2Afn70xTPshgvCz7eG1yIr3AsSTdwqZRuPAIuA1TBf9+pjdINs6i6+v8obejJqTDynHuvnePQtRl+C7XS+W+gVn6btk9p07h9m1NwUWlSpZDianqocfAXqCsmmOfrt4EMv4nGfNUk/TfRT8/P0a9fzihUfSUANdKfkqxUpFe40FbKsCgcrJXb+lXr/+0k/dXn+OcB0OrcOLN4E1yae+3p6RKB5FfPL96rji3pR+6KyZMXmu/uXYyCefUZr6YpXzyKb/v8/zwAWimNfwUAPW/86xqqqviWJsK/7PV7gPo7sm6stRi1fZKiInTt1c30/k6X8SLlr746pqgqlqpmPEto3V7jw+0e/6e7T5JXTBJNXWs+fzHn5N2MbuByrR/w6HRBXCoenSakpebdzRb3NrsGwPomBHgcF/z9wxNmWcm/uTVkve1TVpqiocGO44L7Bwscy2KnHyCEbyiUjoVGkBcFJ/OUG6NXWoVSa2xLYmubsixZpMXqZjwHsDTawEprQyf0bCiMG2o3cLmz0eFonpEUJaeLjPe3eyBqHFuyN4j44a0Bv3g2paoVz2cZH+z2cSyTi/Xd3QHPJwk/fTTGs20qTUMj9pFC8NFuj+NZxiIdE3q2cf4NjQ63qDQf7w2Y5xV/9XlFK7TRtSbyLLZ7Js82cCyWZcW8NFlcriUZRt6qHrkOniUYxyWhY1GpmlHnVT10bKgVnx/M0YBjwU7Ppxc4TAXcXu8QOha/3j9DqZrQM8C+49s4VkiNycD66sgjXhg95HbPZ73r0yoUt0YRJ/OCR2OfujbnrOM5rHc8yrJmrR1we73Fs7Mly9w4G2dVTSdwcKVF5DncGEQ8PPE5mRcEjk1eaFquQzd0jfOxFASeZJkZUJsUmnZgQLjdbI4c20wYlTITMGFJzvGrJYXRBy5NViq1RmjT+Z3GpiHjO4KampbvkFVmQnhrzSIuKnqRg64jvjqcQS3QVW0Mu6TFonG7tiyjFy50jaVqbGlAvGdL+i0LpUzESl1rcm1oqVmlUKpmrdOmE3mEnkAryCvF8TxjELpYUlLVekULjyuF0sJMYnOjhVtNeV1Jkhua/emiYLtbscgkZQPSKq1JSw1xwTJTrHcq/KxcaY3zSrMsS+qFZpHZDFsBUggWmUtSlKsNh8AYlfQjjyp0+fpIIYUgsm2SvCLzFWlZ8GLs4K1LkgICz8YVglQZ6qoU0A5cZknJZFnwo815lGcAACAASURBVHcGhtKclaRZRexVBK7FWUPBlFtGtz5PS2QtUNrUPc9GAmWtL4B0yzIbGzuvmSYFaWloucu8YpFX2MuCtq/JywqlzWY7zksKpQ2FN5NIKV7VG8DsWoI0q8mq2rBKckXkmgltx3eQQFFUTBOjBy9emzIbECFYZkbf3/I1ZQWeDdOloTE/ORYs4wJL1MykYOJn3BxFJKWi75vnR9YYMi3zipavubvpc5ZW9FumWTLLS6xCICWMQg9HBEzjimWnMnmwpWZRVjx2BHv9kMiz8Rv6syUFNZqzRBHImNAzeZMt6a7qji2NI3VaImrjNWA39ddB+PEiY5FV2LZFnJtIINMoUSitOJgmZGVFkivagcWttQilzfelteDBwYxCVVwfttifp9wJLHqRQ13X2LYkL0v+5v4xg8Bjre1xd6tDL/KaBhqM44JfPZsyXhb0Q5fNXmBy/Rr2iNDwy2cTitLIDbZ6wYrmPEsKlJTsnyXs9CLjrC0FtTAgNS00tmVxOC+YJsUqnsyREqfJJbRriwfHMT96LVM3dG081yKfKxxZXxkhUlSapCiRQnIwT5mlxatNryWJXJv7i4Ks0vzTszPubnYuvH/kWBzOchzb4v7RnB/cGNDyW6vXB57F4SzFlRZny4I/vp1d0JAqbZzFkSZ3OikUb1tv2/fWGjKtrqQng7k3K6VNvNZb6uW3bJqTrDKRW+XlTbNjSXqhw8GZ2dafLS47Wq93fb74FgDq/BY6Wq1rk7P+Fh3sPDHNuuqKRkdSVOxPErQwU/U3j8GwDYxcqOZqDaqxDnw7/dSWhjr6NoruIHQ5XWZkykzS32x0rHcNAM3KqyegiJo4L1kUNePFZb35OWPCseSlY6jrV5P3XNVEdiOzuOoY/xmTduOQbyRCV51DE61Sv5Vt0PFtnrwFgIauTddzyEvNMHI5azw33ozWMlEz4hIIP2/AnH+P37a+DaQrbRg93lvupd8EQOOsNLKvKwCoQNANXOO9cIVu/3dl/R6g/g6tlm9fuolcW7LWCVhve7yYpIgafvpsyg9vb7DZDfj+jREPjmYsX5zh2DbPpwkHs4Tv7A64MQj5j1mJqDX7Z2ZC8pPHE763NzIAdhDyZBKTFhVfHixZpMb5da/faijEAQ9PDMD95NkZi7zive02O72wobnanMVLJknJTx6dYUmLG2sRo5a3qs/SmKTS/OrFnI1OQODZdAMHx5bsdCPu5wtqrfjs5YztfojX5K92A5d+ZPP5ixRpS56MU76TFrSayVTk2Ywij6woCXyHF5OE02XGVjcCTITPD673+WJ/RuTbLFJD43UaYPX+dg+la/bnOaFnY4l61c2va7iz0aHSNY9PYxxpobVmqxuwN4yodM1Oz5iUfHO4QNXGHXir66/qmx0H24LrT87IS4WUkmv9cFVfb/sgaj7enVJqY/Tzzkab7X5Av3RZZhWeLdgdRkghCG2Ldzba7A0jCqUb/W3FZy9mvEhNcPhmN6AfufRCk4X78V6PRycL03X3jK28b1ustRyGLY/v3+jzbBLzzeGcQcszAfJCsN24OX+81+NwnvGP6Sm90MazJa4r2e4HhK7FnVGLw7OEJF0QeTaRZzTF270A15ar+jJR+K5N5Nr0QzNlDlyb99Y7zOOSk7OMyLUJLEno2dwYRVRacb1v4Um4f7DEFobOu971WW/7jOOcfuDhy5R24OLZNo5tvoO1todtCzquxctJgu9YK0pUL3RY7/pkhWZvGPB8kvNwEmOEkjWRY7HVDUhyReg43N5o8eB4xvHSTGYqhaF32xLHsrg5DPnmyCXOMrSCoqoZtBxajoNjWdwYBHx96HC2rNCqRjqSYcej7boIDFA2GwUNSLxmKtUPXISQbLRDjjo587QEIRBa4zVupGdxwVY3ZLLMOZyl2LZE1cZw6Fbkssgr+qFLWZnrxUYaF1BRE7kOZ3HBOxsRpaoZHS+Ni6XSSDANmNyEjVuWAQC66TZXWhN5NmlhgPy5vrssa4qyZpaW7A7MZvi87jmCZaYbszbBoPn1Vxo6vkvLt4izkiJTLGvM9CwpKOuAnu/RDR1jBNZMlOO84iwpuI7ZwBdV3TQAMhLX5D2PhSQtKwLPIgwtjpYZIjXxVnujltFYa81Gx2MQ2LyYJKi6QI9r2p5N5Ecsioph6BD5LqWuidOKSmnWOoFxvM4U72+2CF2XrNLky4IkK2kHFiezgrJWXB/6SCmoVc3SCGPxHIt5WeJJie9Yjeu3Ilc1J7OcsqpxLIuwemXolRQV02VJrWtCzzKNMvfVlOdc34uAdqlY6/ic4xvPNlFRy6wE4VBpmCVlY0bVuPAWikJpHh0rBm0PXUNRudxcjwhdYzClqXk2T8nqmqoWdD2bj6/1uL0W8ddSkleKBycF0pL86J0RTycxg7ZLJ3AIfIvjRU6WKX7y6JTvlH1sW3I7K7EsQWhLJpVGqZrPX87JK4g8h/1ZZgynKs00K3Gkxc8fT3k5y1hvB7iWMLIFx2YujJTh5STh06cTepGHYxtDo9C3qecCaRtn8dNlRuTZxgTMMlPzZzIxEoFltop0O29qdUOHZWG0zw+OFpdoyo4ND8cxnm3z6bMz/vze2qsJqCXpBDYnywzHspmn+YVoODD3Sl5pasewDdI3AKglhXHrT6Dt2jjyIgA7n+4ZUtLV2YlniaHgl+rqCJJFUlJRU7/l9UlWUb4FgJ5vmpdliar0lWZXO92ApycxjmPhvrFxrwHfsb4dgAoDHPRbJlvnn1FYkvpN4NGAQltKfNfkSL95jFmpERK6gUNVcqkJ4FgSKYysx3asK8Gb0zQv3wZAh6HHOMlWGtE3jZx6gcNXh3O6gcOT0/iS2ZUUhsZd142D+hUAtEbgiKtBtHGxNfnSoX35GGxL0Is8VKXpBu7Km+T1JRGob1FYmsf728GbxDSjMqF5NkkugfCO7/D4dPF2AOq7BoC2LgNQx5Js9QPkE8M2IeYS0nz9HARXnAMwv1+5Uk1aweVlmjU1nnM1wFwkJYr6rffieTPnbQC0E3gkZXklAO0EDr3A4Xie0vXNNPiqvNl/6ev3APVfwbo2iLiz3mGeKrZ7Pi3XxLHsDSJ+eHOAI+HxOAZhTIGWSdkAW5/Qt8lLzTxTSAFHZykH84Tb6x2u9wP+Y5qTlyZCwhKC1nHM6TJj1Pa5PvT57OWEutI8PF2AMFOHP7hRMGr7bPV8Pns5RQr4/OAMVWtezlpstINV/YuDGY4U/PzpGFsKRh2P/+m9TbqBSzuQnCUpjmXx2fMpw9Blqx/S8nr0QuME+X9/dYwv4clRzJPxklujNpFn49qSzZ6PrkyO43wJx/O8AQ/mJo1ch6KsifMMiVgJ/i1pNY59HtQwjU09fs0Q4FxnWylI8vwCTblqprwmXkFgWWZjAazqXvMgqVSNZQvKN+rm/V1avqGUnoPn0LMRQjCMPCwJd9c75AqywkTNhJ5NnVeEns2djQ4fXetRLk7ohS6g2eoEZrPn2Xx/b8g0LvjrL0/othw81zQHdvohvmPx8bUBUgj+tx8/JfAsbGke7HuDENuSvL/dM27OE0MFlsBWN1hNmd/f7TLNCw7mGVHgEFnWqh4XFe9uN/VFZiZ1tTEZuTBFFpoXs5jAsnClZKsX0gscMmWx1pKGrrwWYQmBL6XJ8g1tIt/mxjDi6TjmwfEShckiNSDdoR047A3Cxhk1JSkUji0MFdu3GUQWW92QH+z1eXYccxRnWBaopu273vXZ6PrYss/+JOOXLybG0bWGWmg2uiEbHUPdfDZOycqxoe3WNbYQbHZ9Njo+vm3xdGz+/uv1jU4T2bTeZrzIWSxLHNfCkjWeZerbvYCyVCzSkhfjmE7gUiiFELDTCxi1HFp+DXXNg6MlLdehrBUd36LludQIbq+FjOeecc91JbWQDCKXyLMQ0uXDnS6uFPzjY4eyUtQCpCXxPQvXtuj6Lrv9AN8SJGUNQpGXBvCGbZuW73BzGPHzp5Ikq7BQLHNFmhuXYc+12esHfPJMolRNUZfG+MwB2zJu0dcHAT97po3rJBVSSVzXwhIQ2BY3RgFfHpn7yRj4mLoURl/73laHyTInyUqUUqR5jbQltm2cY3e6AS/PfPZPEyzLolQVk0WGKwVPxgnXugHDMMCzlviORVkZt+J+5DJeFvzpu2ts9wITUaJqVK7IczNhFBht3M31iE+fj8mVuYbyypgJRa45R6N2gJRTc/3UNVqDJyS90KPlO7y71eWL/Tm6qCilIi3N5oXabFa2Oj6uZTwF8qIiK01mqmsX1IIL9aJUzGpYloqhrrEtyU7PaOuPVUGaG3d0CbQDB9+xGLZcELXRc4sKRc21QdQ03yS7PR/bgjhWVLVivChY71R40kQE9UKP9cjjyTimqA0VthvOV47xe6MW17oRLyYpUsDDk5jAsdnth+x7KbeGLXb7LZ5PE2rg+TghcC3ubHYaN3ebrV7EaZqT5Iovj2aMFykfXgcQvLMWsbfW5ijOSArFg5M5Z0nOe9s9XFtwZ6PNB9tdTpY5y1wxXuT89OGYybKkExoa+1bX5+GJZJkrTucZP75/yoe7PSLfXIs7HdNoqJRmmpZ8dTAl9Gyzibcko8jHswW+K43z8fIiTVkim2lvTV5xaVPqOxa+Y+6T8/ih15cAQtcyUV5SrhoX8Ap8WUikDaUyjaK1tl5tatNCERcmpuYsNnFEO/1XLtaVqrEsiWOZhs9Vm+Ze5DHPiysBaCdw6LccTg5T+qFHUqiL9FNL8s56m9O4aJqZl3NEwUzPXFtgXwEawBy79Za6QDBo+QSuMYl8c4WuzbDtg9BEjmN+g15bHd+hF7oIIWhHNl3/Is3XtgRrbZeyMrrwN8GbbUkGkcdZmuFKyckivwRAh22Pw0XKMLJMTv0b52izGzAMXTa6IUpfjmQ6bwS8Ocx4/TOcn1e78cx48xhGHQ+ta9ru1QA0sC1qW+A5V38HdrMfkG+xubGlBFE3PigXl6GiO0zTnNC1mCwv0pRD16YbulSqZr3jssjVJQC60w9wnprG2jKtLgHQ0LUZdQMsURM5l4/x/Byga6IrzkGpTGRZ3TRS3wYwtQCZXgagVdOYDZrG3tu+w1RVoAWHs+zyvRQ5jI9SBlfcS6Fr8+Fuj4NZRjtwmaaXJ+W/C+v3APVfweq3XP7yD66htcaxbNq+mR62fJs/ur2GZwk+eTZhvMxRuuazgzl/cGuNYcvj9nqXYzfmZGkCl59ME5K8alz/XJAC14KTHJKi5OUkZpYU3Bi1uL3eoSifU2nF4aKg77s8t4yBxXrH54PtHn/1+QHLzJgI+bbJhPyjd0bc2+zywXaP//LlEWml2Z8kjFouJ3OX717rcW+zy73NLn/1xSFVqTiaZWz1AsZxwfVhSC80jr2lgnRZcLzM+eWTKaeLgr+4t7HSXSW6YrnQLJY5v3o+ZRIX/MnddbqBQ6mN8Y/WgnlW8OBwQa3h7mYHMBRMs1k09YdHCxxp6MPndQlIYZHnJQfThJ1+aDJSAWqTx6pUTVFoZrGhlb1ed2xBURlKVtx0uc61d45tMmGXaUxV1zw7XbDeUJC7gcMo8hm1fb7cn1IozRf7C3b7IU4zZR61fTbaHosCOpXmxVnO95Si45qHWMu3eW+ry19/fmSop0mF9a5Y0W9d2/wYRoFNVWm0MNSuc+rPuZvzettD1QK0NiD9jSnzw5MltrAQ1Be0xnc22mhqHp0sscTlKfRGM0W+vd6m0oKqqtjseGz3Aypd0w0cPEdy7zWQ3g9srg3M6/uR20QrdXgySQhsC88WbHYDhBD0QpcfXB/y6CTmwbGJYfJsc+90QwOQv3ttwJPTJT99NKHl2wxbHtcGAbfW2oSuzVrL53ie8uh0gWUJImlxd7PDh1s9HFuy0Q44nqc8OJkj5av6Rzs9pBBsdcNVXYhX9dcjm8ZJxuPTJe3QwZeCW2stfnB9SA1sd0JC1+bxZIFjGTOk66OQOxstEJLv9AOGkcOnL6eNBlFyZ73F3sBDiIB3tzrsT1PuPpmyKArySjJs+2wPQjxLstUNcS2LD3bOOJim6BoGoctm20zjN7sB72522O1HHC0KQkfS8hwGbZfrg4jNbkBeau6O2jwQCV4T8L7Z9dnphQwjF0Yt9joRy9TopB1b0vPg7kaXYcuYMH19vOBLMUdVGq0NY2DYchm2/IZKmhOnauX67FuSYctlEHm0XIdHxzFfHcxxbJuy1hSFYnuzw6Dl4dkWJ4sWv3h6ZnSOZU2hzHQ5rzTrXZ/3d1r8w5PTBgxpwHTW56oicCz2er6ZJKuaqqo4SXLW8wpR19iW4OYgJHBtitTook/mBdf6ihozjf7+tR6/fDZGqwLqmjSvENLoaQPX4u56m62Ox7NSIWrIMkWSVUSuRds3DuhSmiiUZa4IbAen0do5DbhyLBOHlWYFnuOSFcb8y7YEo7ZPy3EIbWOYpXVNUmmmSxNHdXPUYqcdsEzn1NrEUy3SArd5Fnx0rc9HL+b83f0T405eGDvsSmuej2Pe3+mx0Q/57GCOa0myquRonnJvs8uzScreqMW7Wy3+8ckY4QiyrOLFLKEXuXjLkmDL4oPtFj9+dEJZVRSVyc+2LUHHc7m+FnJjLeBnzxTztCIrKs6WBcKWRJ7DB9sdPtrp8OXBlDzPGcclk7gk9B06nsOdjTY3hhGRLYmzipO05CePxizykkHk8eFul62uj64lcVFQlTmfPp9iO4KNlo/SsN0P6dgux3nKLC74m2/GzDLFdi/g1nqLfmSigc7iAik0D07mtH2XdmD8JHzXJnQs5lmFJSSn85SzuFhp4QPPouO5nCxzhKyZLAuK6hXABEHoOcySgrjM+fpgzt6gdWHj7rpGs79IMp6cLrGlWElvzBTXMRFmhebh0eLC60PXph+5HM0SQHA4zbg+bF2c2oQ2+9OYzhVTm9C1+d5en2Wm6Pq2GUG9sbqBeW5UtWYUeRdixc4/4zD0TJPVta7OqIw8EPVqD/D66gQOmz2ftKoYhR7dwL1U/85ul1lasdMNVlr18zVsefz5vQ1OlwXbXZ+11yaX539/qxui0QTWZYAdujYbPZ+qVmy0AwLXaDxfnw5eGwScLDJGLZ+rGJnDlsd2P0QCg8i/dAyhaxrsaVHR8d1LIDt0Le5ttqkmFu9udgmvOMfbnRAhGpB9xTleb/vUQl/ZRHAsyUbHR6MJnbfU2z7I+sp66Nps9QMmcc4g8vFd69LrbwwjnhwvaXkujrxMM+4EDne3O7iWILTdK49xtxvgOALPvvo62u6EWBarPc/rSyAYtjzjeXAFyD5vhHiuuBKkrxohaHzbvvAd1bWRUo3aHkdzxXo3oOU5F66T0LX57rU+StVsdgI853KzJfIsWoGNZwu+XSf7L3f9HqD+K1l3Njv8r392h9M4ZxR5rHfNg7Pl21wftdgdtDhZFliW5NfPpzybmCy8P7k94pcvLB5OEmxbkGaKk1kKmM5/P/SwhTAURinRtXrDFUyAkBSF0WQIS6yoTY4lGlqjJsnNpKfURr8D5kFpSckkNa6JT08TfO9VkHzomqndJCmZJwX704RZ3NDUVqtmWWgWccnhIiXJKz7a6bLe8UnKCktYOGiWWcX+NCHOKt7b7rDVDSgrTVYo4rLicJ7x8CQmLjQbXZ+1Rmdb1pq0LHk5S7m/Pyev6mbKF6wA5izN2Z9l/P2DE+Jc8f0bAzY6vun0BQ77k4x5lvOzJ2OWuWKj6xsQFLjs9kIzGVGKHz+aoFbn3W1cCx2+PjAGLj95eIaUkkHLN53cyDXTp6cTfEfw2YszBqHNRi/ko50e/chMmXUDlM2UOeb2unylIRKApNFQlkZDGZn8RTBd/CRVYGmWqWZ/mjCMPALXPj/9LDJFUZkN6Vli6Ci2lKspM1qwKAuqSnOyyLjWD0GIVd0S0ugIK0VWVKtm5/kU2bctDmYZpao4nCVcH7WQzetHkc+w5fPVgQHp3xzF7I1aKyp4L3K5s97m/kmMa0uenKXcSctmogxrHZ/v7Ha5f7RAC3hxFpMUfbpNvd9y+dGtEf/0dEJeKU7mplMbNsff8s2kerPnYzUgfBAYE7NS61V9qxsghERQ0w/ND2ZWqFf1TgBCorVpAtiW6TDf2WiTV4ovny9AQlFqo5ezJIXSRL7NTj9kqxtRlBWl1FzvRyuQ3gtddvsRt9c6nC4yClnhOTY319oGEHs2W52A2xstvj6YE9oQOQ67/YC2Z6j2W72Qj7Y7HJyl+JZE1zU7fZ/Nbtj8UA54eLLgP/36EEdKrBp2+wG31loIIfhwu8eD63MOFiYn07clo47HtWFIN3DZaod8fW3Gy0WK3Rxb1xZcGwQNXcvh3c02948XSGHhW5Ltfsi7mx06gcMw8nhvo8XPn5wiaoFjGwflD3d6DFsevcBlvEj4xbMzXNciySu2u2Y6XQvBjWFIXcM/PpmgqVlkFVJYhK5Fpmq6gct3rg25uz5mllSkVUlVC1xHMrTNffz+bp/rw5DjRUFRWFSahobdTHG3e1zrhygSdG0mDL2WQ+Q4uJbF7Y02vcBhllZopSh0DU1chGNJdvshkW3jOBZlUSIwjR67cfPeHYSshx4nbkGe56RKsUyNcZpjSW6stbi91mqmESaP17EkpaqplLnOtvsRX+7PKLWheVtgDLQWOR9fG/DlTm+liadS5EUFCB4cLfjzexu8u9Hi7x+e4ggDgl5Ol/hum2dnGe/vwJ/cGfHzR2MqoUlTzTyrqJVxHa5UzfvbPTzrOfO0RCttnJFLtYqU2Ru2CBzz/WlNA87M9+RaFt/bG/BfvzqhqBLKxvVa6/rVORhFWLWJQdJKo2vJ0TRFtc1EstP18R2bUucslznzZc6obcyw3t3qsDMI6bkWJzUkleLByYJB2zXZl7XJ0AaYphW6rPimrrk2NFKPc3O9vUGLJ5OYqhJ89myOZVnsdALubnXwm6i0WVxRSMUnz2a4jsVuP2KjE5gJZeiyLBRFWfLpyym7w5DrTQ5lJ3DY6gQczTLyRnYzavl8uNtrfs8tbgwifvp4wmmc8fPHY6hhrW3c7Ictj72Bz/Pxkrqu+dXLOaNOwA+uD5DCvP/NUcTDkxjXgk+enXFtGK4AbujafLTb48lpghSCR+Ml7+90L5jLRK5NXioeLXO6ocN39y6DVKWNwYvbNNxe1825jdlXVprmzFXAwnUkWVWtJDmvr9C1+Xi3x+ky5+awdWnK6FimAX2WFOwNokuaPceS3NvssJWVrLcvRwmFrs1OP2CZlwzCqwH2rVFErc1zxb3iMxp5VYQjBZ3AufQe50C/ro1XxFXH8P52B0sKWq6L61z+jHvDiANPcmMtvPL9dwYBSVGy0Q7eWo/zt9e3+6a+1rpYr1evD5mnOV3/slnX+TnWdd3IftxLf2Ot7fHRtR5QMwj9S42E0LXZGxhzuX5w+fXnn1HV+srv6fwYs7JireVfqncCh42uT1qUjFqXmwTnjZCkKBmGV9fPGyHrbe/Kz//DWwO+OTS/b5F3+Vpfa3vc3mjjSpNde9W1ttePzF6okaj9rq3fA9R/RWu966+A6etr1Pa5PQq5f7xkEDp4jkWam83zn97bIPJsvjma8/I0JlM1/9fXJ/zg5hqb3YAPd/s8O1lwMMupa80shrOlAbDGmdalpsY27EbS/BWALFWN7Rg6oKqNCyXilfbA6FxqqkqRl4Zi5FivRPqmDkVVkTRT1F50kVpkpgaKRV7y7CQmCtyVdseRxlY/T0uWRcX+WcrUr1i8BnClLclSzTQpeTpZMlkWfLTbMSZErs16O+CLZU6cK745jZkXFe9utHhvq2s2152AJ5MUpWqeTlK0PsOzBTeGLULP5vow5OVpTFXD/eMllpTMi5IPtrv0Ipebw4hPX0yREn71coIlDa3x9nqLUasxRUoKFPBosaAT2QzCjFtrEZ0gYqPrmwZAqTg4M6YZJ8vCxPQ0LphlDWfnU+anZ0yWBX96d52O71BWmqTUlEpxvMz59NkZs6Tkj++s0Wv0iXFh8hNNfUqSVfzwnTWTJ6s1FgKtBEfzhE8ej4nTku/dGAImDqkTOCS54miR8XffHDNPSr53Y8BayzNa417IN4cL4rziHx6MqWvBjbWIYWSA3HY/4OU4QdeCnz2a4lgWg5ZPNzAg/Xo/4BfPXoH0XmAybzu+QydwWOt45FlJ5QhejhPWWy7Xhi0+2ukReTab3QCr0aA+n8R8uT8jKRTvbnbNddToSSsNeV5yMs9ZbwdE7qvJRFFBUeWgYdKAdOe1rnhe1eRN/Swx1D6rmaQ7ltGGLtOcZV7xfBKbTnvHbIA2OwGjrsf+JGOZF3x9OCP0bLZ6Af3QpRu43BiGfLk/B1Hz8DTh5kZ7BdJHbZ8bI5/nkxhbWrw8y5i9BtJ7kcu7m20+fzlDSsGLs5g0H9Bu9Nwt34DQXz6fNTTkiy6c/ZbLH72zxpeHSzxLNu4X5thEU//RzRFfHiwMtauuV1RvVdf0Wy5/dneD+8dLVFXj2ZIN5qtJ+vnf/9WLmelW1zU3RhG31lpU2hjlXB+12GqHVLVClXBj2LpQf2ejy93NNuM0x3ckncjoVs9dvW+OWtzb7PB4vMCVLusdn2HLoxMY1+Wu7/L+To9Pno3pBD79wGPQ8tjsmOxmv23xb99Z46++PMKLBNSGqrnZDeiFDoFj84fvjFh+dUShNVYNvmWx0fXpBA5SCO5s9FiWirLQ2PYrPbZjSULX5vp6h0lRkmUWGslmL6Dnuys62EY35MFpjGMHKKXY7oVsdAJDL235bHQ9alET+S5Jblyp+6HRs9/dcHlnLeAfApue4zbOxjk7g4C0NGZf72628F3bxA4tC1JVM2q7xI1T5s2NNj3PYVkVWAKSUpt80qQkKZQB2YHFi3GB1hUns4zn7YTQdaipG98At/n9qFmmJao2ALpSje7XsTgoFHlVkZaK56cJi6jih0rT9T2oaxZp9Y9gpgAAIABJREFUhapKhGV00Gn56vfCsAhqKqVwpJleR87r97EmTgrKqgJhczJ/FcvmWBItjPlcUVUUpeRwnuFaFv2G9RIFxvyrqmumcc7j0yWVCvlw12iGHSk5W5YUec7pPMdyJKednI2ej2tZXB+0OJpnnCUZP354Sl5V3Nvs8m/vrBG4FoPI44uDKWdxTnr/lLxS/NGtdW6uhXQDh3sbbX718gwlal5OEv72wTGlrun4hjraC1yWSU5SKL46mOM5NjfWWquG6oc7fX7+eMI8N9Fz233f/JY0DdNR2yVNCvaLgm8O53RCm79s+UaD3JyjRVryYpYgEGy0/Qv6wFlaMFnmTNOSF5Ml74wi/izaXAG9WVrwqxczjmbZ6rv/0a3RK7dlpVemcec64DcBWt93iEuB7152oC2V5mSZk+QV+zPjuHwuCTqvH82NQ7OuNTu96EL9/JkGNKkF1coI7/z4bwwjZlnJqOVdaUoTuQ4311r4tmVMB9/4P65l4vRcSxqm2BXvEXo2vmNdSeM1zwsHaq4ENo4luT6IeBRKboxaV4Lw64OIZVGy3b1srHNeX+Ql293gW+sbV4B4xzJxf2lR4TqSk0WGa1/8O5Fnc3ejg9cYYl71HjfXWleeo3Mjp3OAOowufw9mUh2idH2BYv16fbsbMMuKK82FQtfmD24MOItNI+PN7yF0bX5wvc80KdnthVc2EW6ttVjm1ZWNDjCNive2uwSOYcm87Tq56hydNwJur7dW6QtvAtjfhfW794l/v/6bV8u3+V++s8M4LtB1Tds3Bkjnte/s9tnrtXh0lNAJHE4XBQ9PF/zg+pD/+b1NPu+4zLKSk0VGqRT/+csTPt4bsdkN+Gi3x9OTBSfzAtnQVZ+Ml/yoWqMbOGx2Q0qlCT2jdRAa4nMAaVmMWj6LvEBYMI1zau2ybCaopu6xzAtE4+6oa8HjkwXf2xviNJvLZ2eKUtcczBP6Vc3j5rN3A5eNts/+NAaM618NvJiYDLnzrNgXZzGVUjw8XLLW8fns5YzvXhsyavtcG/j84lmFI+HgLEHrms/25/zgxohe5HJ3s83Pn47RtWZ/HGNJwTdHCX+cFPRCl7WWx6IsWeQlp9OMqqo5WeT8+Z11Bi2PjZ5PVlTEVcWLcULHd+m4LtPEPBjXO36T0Wh0UU9OY8ZOyexewW4/4jw+Is40i6Rgf2amzBdy/GpWU+ajuYm6eH+78ypOCEFSambLkv1ZRl7W3Ntqs90z14hlS9K8YhZXHE4z8qrm2ihitx9CDbYNi0XBcVzy5DQmKRSjjsf1YUToGA3WF4czFpniwUlMpcG1BXv9qNEaW4yTlFLVfH4wBQFHi5T1pnO5FnksihIFPJ0sL4D03X7ERs+nqBRJqTk4y1jr+Izjgpsjk6sLkFSaLKs4nGU8OImZpdUKxANkqiYrSw5nGfePY+aZYqsbNBMU44j5OohfZiX/wztr9L8FpH//pgHppTYb/LyQHMxiPnkyZh4XfGevb8wtBLR9h0Vq4mTuHy2pFVjSaElDz2avaXRo4Kv9Ja5jTIPWWv6q0fGrF1N8R14J0t/b6vJ335xiWTVfvJyx1fW4NmzR8nqE4WWQ/sX+lGXe4oNtA9JD10YpmGQpcVHx6HRBpfUKxIeu6VDNkpy4qHh8vEAAN1+brmgNi6IAbaKFiiYjFMyGq+u7TYSMIlWaQukVHT50DdVsmVUUlWKZFsRFtap3A5e1jsfBNEfIipNlyskyw7Mt+qHLVi/k470ef/f1KaEHD46W7HR9WoFHP3RZ6/j88Eafl5MExxfkpWLQMhRmx5a0PJu/uLfGV/szHNvU11rmOeo0uuUf3hrxj48nCClA1ewNA0Ohc+3V1KLtWlS1RWAbs693t0wjzLMtdgcev3huJqeOrPEdyXrHa5gY7qouLIELKy1yJzAxMx/tdPjk+dg0/bBWruKdwDHP+Z0+f/3VMXVdUypBL7D4zm7PaKeBe5tdk5FYKBOr5bvcXmtRaXOO19sBo5bLPCvxbUlV1biOXGVqbnYCBi2b6aTEty2K3ETOtH0bxzLU7VHoMlkWKCUotfnee6GNMJ1LLEsipQGRZaE4S3Lq0Dh1O5bEFoA0RjQCEJZ5flVKU2pjYONZkhIL2xK0fWdlnFcqTVWDI6GsajQ1aWkYNOfLd2yysiZVGkSB70h8+xUNc70d8mk1M34FNeRlxSQpCJRuDGw8ilIbNkgtWGR546ZdMQg91jo+utIm+qsqeTlJsBA8G8d8uNtn0LbJioq8KMm05Okkhlpwez3i1nqHd0Yhf/M1KKU5XqR88lSjVc3+rMWPbo24td5CqJqTeU6tMk6TgpZn0/ZdfnhrwFbfp1I186xAVzW/eDJhvdGz393sELkWaa54Nk6QCD55bHF9ELLZDeg0WtpMaY7nOVJYPGo8Kc5lL0lRMV7knC5zLCF5eppcAJGVqjlZZDybpigFnz6f8fHegPWOiRGapQVf7894PktQlWAQutx7ze04aQwbX06WKMC2LP79Rzur9y+V5rP9GftnKVIK0kLx/WsD+i139foX4yVxofnkyYQPdntsdX3ubHRW9YNpQpwrfvl0wrvbHXZ64apeKs3pomS8zPjZfMy1fsSw5XJj1OJcueM0/hNxbvKa3wS45wi3UIo4F8DV9VLpJn/zVb2ua4QwmsW8NCZ0b77/OQCMi4pecLUpjgHV4hLoOR8eOJZJUnib6+t50+xtU7nz+ltdY4WhznYCZxWXcxVIvHTuXluuJQkaOvPbj1G+/fW2hYAr66XSTJZGt3kVgAZWBmpv0/pGroMl5er5etXn+9ZzxG8+B99WN6yRFqV6lSbwu7Z+D1D/f7JurLWupACDmXD8j+9v8PWxcYYTCNzGKOj9nS5rHY+vD2OeTVJC1+Zwlq0A7L97b5PHI0OXfXA0Byn52cMJf3J3g61eyB/eHBA5wmwEmk3ljx9N+N71EVvdgNvrEWdJxkbbM+HYdc1Pn5zxb26uN1OWkP1pTN93yJuH8qcvFvzJ3ayZYPp8vm82a4u0wrcrfvV8wZ/dzQg9QyX59PkZgS05XmRY0uIXz2b88R1j9LTd8/nZY03oW8RpSbd0eXpiHH9vjFrc2+hQ6xohaiZJRT/yeHA452CWGGpn12dZlFRKMUlLNnLF/tkrHW4/clkWFXmmmBeKSmnmWXkBQKalZpaULHLF8Twn8V9ZpzuWxJGSXBXEheJolhP7F63VLSHRtZmiHs8K+lF9gYbtWCYmZZFXPD1NCP2SyTJfvX8ndBgnGUmpeHy6ZBqWTJfnNGtDAT2LcyqtuH+6ZJiaHL+PdwfmO+gGPDqNEVrx6DRmUVRsvpzz3WtDepHLjWHA395XaK14OTZ5p4Mjnz+6XbDRNZv4//TrA7LCHP8gdNF1zTjO6EUdNro+ipokNz8aLyYJZ4uKWfI6SBcrkH4wTZg3IP0a0YXzvHyjfr4kgqwyr385SVikFT+69apuWZJ5A9IPphlZVXN9FBkTkbeA9LWux94gWjlWH8wSplnFFwcL5rlCSrg+bBE6RoP2zfGcpFT8+vmUZV6S15p3GtOvtcgjrirSSnEwm+F7grTUvL/TY6cfstnzKcq3g/RzvfYiLy6A9HM9N1wG6bO0YrcfMGh5Kz12oWpO5zkPj5ZM44qtXgPiGz12peBklvH10YJloVa5ym8F6df7JrZKmdiptKp4OUk4LTX/5ctDvrvXZ6PjN5P2iK8P5iyykv/8+TEni4Jrg5Bh5Bm2wiji8Cyl0vA3X52yyBTd0OXfvefSb7l8tNPlJw9OkRLuHy64NgjphyU3mnNwbRBhHBkF+9OER8cLSlWbTZ5nwFTgSAoF8yzn+TgxGxDvVX6e51gUVc2yKDmd5xz46ar7PWx5zQQZJknGwSxtaJOGXvnBdo9/eDhGaDiLcxZJRVkZl9CWb/PBdo+fPpkgtSDOC7qBze2N1mrzcWezw7sbHSZxSVaVeI4xCzlfm92Adzd6PD1bgBRUWmCG7KKZKER8b2/AL55NCH0bURvwOYhMVumttTbf3+3zt49PWesG+K6FLeHm0DSapBB8//qQ/cVLtGWbCXLPY7NnqOCBbXNjrcP9k6WJf7EEg9Bh2Gjxur7Z6D8ex7i205h9RWbCYRlK8631Hi+XOVluHJ5HkUcn8HAsC2ozVUDWlGWNkALHEviObDaDFsN2YMCXMI7Dhq5vAGjo2kS+g7AEAZK40JzGOaFrc7rIuDlqEQUWNRpbSvJCc7LICR2HcdPwvL0e8DffSPJKoirF4TwncFw+ez5jtxex3XbRmCmgqmpmSUbVDThpnrcfbHf5P37xnLQ0USCPD5cIBD99eMZmL2RnEIIw9Oi8qokzM4FRGt5ZbzfeCjV5rqm0ATAHs4wkN/m1Hd9mXpRMk4q61kgJT8cxhTLTftcSqLpGKdC1+S37fH9OnJbc2+lx7uS9SEtcqTiZJnx1MMO3LYQ0kRzzsmSyyKkFfHM4Y9wYAYExn0kbEOsIeHI85/kkXj2DKlUzzUomyxJR13x1OOPRyWJVTwrFeJ4yTkpUrflif8r39vrc8Q2AnKUlJ4ucZV6wP0+REpKs5C/e20IIA0DvHydMlhkPThYkRcXOIGSt7dP2jc7vZF7w6GTJV4cznk//H/beY0mSLL3S/JQap27m5jTcg/PMSFYUyCJAodG9mZGB9GKWvcFzFF4DDzC7kW6ZmR4ZdEOKoKoyiyTP4BHu4eHUOFHOruosrrlFeEYUCkAPBI2evNs/1MPsmpraPf85/zku11frtCt5akVj4W58NHH5/GDGXs/h5maNdiV/BoQ/7lrsjeTM/c31GjfW6guAkIiU3YHD4cRFQ+XqauVMPRYpzwYOzycuxu+pH4w9elMfTVc5v1Ticqf6CgDpWQETVzYoXmFKFRnjcmrS9Qp4mTcro3nUziv/5hRkJylibuD4unqYpITJq9ebmgqKwsyP0BX1lciZU5b6D0XV/L6lKAoK2T8scPY1KxbSB7pg6gun498H8E6bBv/o1zh/l7/vPSr8gb+pnOadvhip+ur6+wD6v4b1NUD9/9H6fRJggDc2GvzoxipWEFPNG1xoS+bD1FXWG0W+d7XNp4cTynmDNMtQMmUBYLdaJfpuzMOuQ8XU2Z957A1stltlfnBthXY1T5TAzx52qeQ17ndn7Awt3t1u8T+/tUExr/HLhz3uHk/JaTmeDWz2Jw5vbjb5d7dXEULw4Fjn88MpKAp9y2foBFxfrXNlpSKdgoHDiYeqqAztYFFfrpiYpkq5oDP2ZKbq3tjlZCYNjW6t1fivd0+I4phRAkEieD5152HbEsCVcwZJBkNH5hF2nXAxR+vHgoKuQxV6VvzKHG4spMlSqkCcwMSPCLMX1u+xyFA1CYBk+HaKEILgJft6VVXnPwSyY69r4CxYaNmh258KEmBk+6QiZeJIaVrR1KnnYCBSMlKOJx6tWoEHXZu3t2Sc0Fotz4OTGbqaMZgFaIrKgxObt7dbCxb580NBXleYWD5FTeVZ32PqRdRLJhfbJT7Y6WPqKmMnoF4wOBi7C5C/0Syi6QqqquJEIUmcLkB8pyYjaQxVJUEacU3ciCRl3j2W71FTVEKRSMbCjoiLZy36FRQp/UpkluhX67quAhl+8rJUXCz+fmUO0kORcjzzZbaoHy/2cLVWYOwGhCLh4cBiyclzuVPkjZdA+tOBQ5oKng4cpn7Mcr3AmxtLi0bIx/sjSAW7PYswkS6R370czRnQIr96Kl/P8UQqGXb7DiMnYLtdoVPLkyBzKqduxMiJSWJ5n643JIuXAjNfMHNDDsYuU1sy7acgPc0y7FBgvVy/cpZpdyPB1A3ZH7rMnJjvXJL103lsN4rpOyFPBjZDO+Kd83UutiuLeWw7ihh4MY+6FhM75vpKReYqz0H60dRl4sfcO7YkSNdgqylBejmn07dDrEAw9uCzgwl+lLLVLC9cvUeeT5CkPB5YaLpUVVxfq9Es5WiVTOwoJkwz+jOfekm6cL59rkGj/MJUzQti+pbPo65Fs5jjne3mYgvCl0D6V5n2Uzm8P59ZfzpwsYI5SC/n/uBMO0ACuGHM4eR0pj1dgPiiqVPNG3RnPj1XSrnTDIp56WYr53UL7A5s+l7C73ampKnCZrO4mGnv1IqcWGNiAV/sz9BVlVrRpF4waVXyXFgusDO0qBg6vZnHbt9he0neH/WSyc3VKl8eTjA0laHjMfACKvODd6Ns8v61ZT7vzjAVlakfEcbp4hhVyulstYrS4ROFOBWYukan/IIFvrFa4ZN9U0YMZSmVgsnltgS49aLJzbUqHz8fkmQ6QRzLme6SsZBarzZNzD0VYWioaYqhSYfepbI0g9lqlTmYyVnmTFGo5FS2l8oUTKnWubZS4tnAxjQUvFAw8WLa1SJOIPPFb6+V+WhPx48y4jQjiDJqpRx2IL+b72w1+emjHrYXEsQpUzfm0rLC1JPg5+pqjVrJJIgSgkQwsiI2GoKeJeWo28sVcjmNqZ8gsoyxFxMKQXcayLEATSWZu3qKFNxEoGsqRxOf3synXjBRFQ1NUVAVmZE8cWKE8AgigaEqjPwIN45JYogM+TwT84alFyVyhjiVMv1eHHH/2GJoy9EWxdTxEoEfSQm4HSZ8djDlZBqw0iyiqwpJlpFmYAURXx5PMb5QuX9k82/eWJHPYkUhyyR4OZj6/PxxlxQppZSNKMlceXHC7tjlF496aKrC8lyOXs0b5FSwg4Sjqc9PH/TJ69I4RwE0TcELY2Z+wn3V4ldPB7RKeTRdIREZfpxwNA7YG3sYKOQ0ndubAe2KZI+zLGPo+ByNXDTkzPC3L7ao5KVhT8FU6U5denZAEKWkicofX25TKxpzFUfMR7sTHvRn5HUDL0m5c655Rsb8m50RD7sWiqow8+OFGgrAjxLuH0/58kCO9kz86EzdixIedWfc3Z8hlOyVepKmTJ2QnaEHWUqaiLkM9QVbOnIDHhxb5HRl4Tz8chSNFyWc2JIlzhkaF9uVBcsMEkTP/IjdgY2uqBRz2oJFhnlkkR+x05f1Ul7WFeVsfXdgo6NSnNdPFTPSYNHkZOqT0zSOp95r5cavk1EvliLrvxdk84KFfl1dAaLX1E8TExQFeQ4sGK8Fkf/StkOxSDmZBRQNDTdMWG/868s5/UPra4D69QLk4eMv3t5kFkTU8uaZhxXAtdUa373UJkoEpq4vJMKyQ2NyrVOlXc1RNOWsnq69ONR880KL/szjVzsDdF1l5ETM5h3j9WaRf//OFroKj3oumQIHY4/B1IdN2GqV+Te31inlNB6eWCRJwsHIlTEQmyrfutCi74Q8PplJCW8mpU+nTsS31xu8uWHzvG/RnQZkWcbYDnHmAHOlVuDOVovDkcPQHaFrCpb3gmGsFQzWl8oULJejsUcsBLYPfiSvr+ZNGiUT4WRoGmQq2F5Mf/bSnG7eQCEjZ0qWRcl4CfxoVHImdhCjaiwMQPYnL8mQq3m6lo+pwdSPSMl40HN470KyAJhP+ipFXUp1c7HgN88mvLvdplXJ0yzAyJUZn5YXk8QpewMJINfqRbaaRUxVsjQ9O6IWCR4ezxYs8ZVOFdIjdE2hbwtqkeBp32boyJnX5Wp+bvwio0bsQPBs9ALkA5AoqCpzFk+g2MGCBY5FSphkCJESp2D5CVnKmfrpLHKYgBPGqIpylkXWFIJEEM8PThkZtv+CBV6u5DmYSFlY3wrIFJWDsbuQgq9X89w/ngIZQztAVRTud1+A9LV6jt/uxpjzKCY9U/ny0Ob9K/Lgs9Us8JM4QVMyunZAwdB58hLTfrlTJRWHoGSM/YR6kPBsaC32sF3NEYmURAhmYcLADvFFyvQlljeJM/wowYslc+PH4it7mBJEMU6U0p2GuIWz9SiV4MuNU04mPk5BnDEc0zQZteQn0gzLzSdM5t+DoqmzVMqzN3IIE8Fuz2G5Kng2cHhrU+7hciXP54dThBA8H3p4Ucrdoxlvb8k9XK7m+NXTkDgRPO1ahHFKyTT47iUJ0jfreVw/IooTeja0RhJovFAz1Pi/vzjGCxO6M5+KYRBFKVMvYqVWYLmalyA6EIztgL2BS6lgnMmjTLMUN0plI8SJSEV2Nk/yJZD+fDgH8ZdffAaKItUgUzdib+gwcSLeOd+QIB0ph//7Zto7lQJ37QA7SHjQtxl7EZfbJa6u1M40OvxIsDP0iJKU5arJjdX6Yg9/szskERkPezMMQ4L0G2s1GqUc55eKfLjTJxIZR2OXxhykv7PV4NJyleurdf7L/S52JJUApZxBdxZwaaXC+ZbMWdYURUapOD7P+i6eLyQ7V5RAsaRr2JFg6siRA5kZXJLuonUpKw/TDNeSzualnDSBMnWZkVzOmUSJoGfHjJyQvhNyac5QbLVKtMoFApHQnST4QbxwNS/nde5sNHlwYjGyI45tn4EVom/Kz6ZeMnljs87ByGF37OAHKQ96Dq1qkVhUKOd1vnelw9Oey5cnE3RNZeaHNIo6mgoo8L1rqzw4sflwb4ipa0RCkKYpmSJfw621Ot/aWuKnj3o0ywpaJp+rw5n8jq3WitxeazB2AyqKQSIy6nkDRdFQUGiV81xqVnGDKTk9JkUyvKcxMIam0SoZ9Ga6nHNNIAgTxoTzuko1p5NmCmkCqQoigzQ7PS5LyaGhaghVkGTSRV4I+fp0VSWR0YuIBFClXDgWKVGUggkZCkkGcSwZsL4VYioKrp9Qyuuo85xlx4NACI6nIVE848Zaha12mZVKgYOBgxumdGceHzyboKoaP7i2LOd1SyYoKbaf4sU+v9wZgaLywxsdqgXpqPyoO8WJU4Tt88mBdFA/rW+3yjzq20RRTG+m8ssnA9ZrBd7eXpLmNFXpSRBECbsDB1SVoS1/C4umnN08mHnMQsHu2EdV1MUz4jRndOyH+EFMFKc0y8aLGDhNJW+oPJ86DK2ATI0p5bWFYgpkhu6XR1P2hy4okFMVvn9teVG3gpiPn43ZGTiAgpLC+1faZwDq/SOLRwMHlIwkTnl3u7GoxyLl4/0pnz6foGkZIyfk0kp1Ma7iRQm/2R3yyd4EP05YrkkjpHpxeXH9057Ncztm8qhHxTQ5mXr84Prq/Dc65WgScDh2+Xh/QqdSoJzXaFfkuMBX661ygVpB1hslaRrWt6RS6Te7Y9kgK+q0K3Ku/3TFIuXZ0CZNFZaKxnwW1Fw8Z2ORstO3STNoFI1XWOJYpDwdur+3noiUp32bLFNoll5//bOhA5mcRb+4XDnjeL1cyfHEi5i4IV6UvALiT5cTyHn4oqmflfvOv5K2HxPNFRqvqztBTJJmv7fuzn1dvlpPRCpj6TTllZiZ/1HW/1hw++v137Qa81mK130J29U8f35zlR9cW+bPb668Yq9+fbXGN84vsb1U4s5mnSudyqJm6irbrSpbzSKtSp6VSo566cX15bzOhaUKrWqOYs7E0HWmgVg4+F1eqXBxuUq9YqLpGgJ4cOLiBAnLtTx/8dYm37m4RKeaR1FlmPazkQR4a40if35zlbe2GyzX8qgKiDTjYOYTJSmtSp53tupc7pRpl02yVIKALw5nOEHCar3I+5daXFmrs9YsoOkqUZzy2bx+bqnEj26scXu9KeVXWUaQCD7YHTFxosWc7larzGq5QJqmBGHKr3fHTJyIVjnPnc06l5ZrbNaLSIWhwhf7lrSar+S5tlJmvZpnrVGSc1poPJoDyHrJ5PZGnXO1Iu1KgUQIFBR6U48Ty6Oc17nW1NleKrNeLyFS2TV8PpEA0tRVLixXaMxnGbNUHlj2Ji5D+yVwUs7RKBcWXceuEyxYZEOT5h21Uh5NhUQILPcFyAeFasmgVsyha5CmAtuLF27RoFAtGtSLOUxNdjD9WLA3lJ/h6Sxy0TAwdDm/6Cfpon4KQPOaTkFXCCKBH6XcPbZxgoRWJc9GPYeSQi0vXSSjSPC094IFPrdUIKfK13E623MK4st5navLVYqGTqVkEs7n1/qWZOpNXWW5WsA0NMoFk1TIe6xrhy/tkXROzpkmzA+NVhjjhWKxB6amYcwNW0SakcTJAjzFIkOfS0gVRQKtJBGLRgcolHMGpq6hahCLBD9KmC6k3BrtUg5T01BUeYjxI/HKHuqKiqnKBoodJdz7yh6KJKWQ0wiimCAS7Ly8h80CGhmFnIYXJYgkY2/kL/bwynIVU1PJ5VSmgYwD2enPFnvYruZRNQVdVwiBqRefaXQYmjJ3bxW4YcbEDem+1OgAEELBj2K8WMpkLTc6C9IF+FGEG2X0Zh7Hs4Cx88IIR9MU3CjGi1MOxy4HY3fRbJJqCh13zo49HzjsTVye9Owze+jHCXGS8PTE4Wjq8eXRbLGHa/UcUzeETMavHE4Cfv1MPgvqJZn56gURZCknY4eBHS3u09M9hIwoSzmZ+eyPPPYGDtP5d7ldzeHFCZYb0bNDdgcOzyfeS2oEhUTA1ImYOCHdmc/zkcfIDhd7EKUw80P605CHxxZfHEwZvrRHqqoSxDFDJ+RJz+He0WxRNzSVfM4giAUjJ+b52OWTZ9I1/vRZUiua+EmK7SXsDR2+2J8s6qcOpmGS4UUpexOfTw/GPBtYgMz8XqkU6douji/YGbr87YMun+4NAXhve4lrazXSFFQdumOfu0cTfvNshBMkbLXKvL1VR1NUqiWDmRPxuDvj3rFNPI8U+uH1DjldI6drTNyQ/kwCYZC/kX98pU0xb1DKG1hxIo2aQgke6yWTNzaqlHM65YKJpknzv4Iusx1blTw3N6rkdNm8jIXACeQ8tqEprNYKnF+SB+lKQSWnM88KLlGZs8jn2xWalRzNiiYl2pk83OqajP061yiRz+kYKnLu209w4wQxd6S+ulyjXSlgGlJe2HdCnOiFWmSlkqeoa6hIGfXA8pn4Mmd7qZyjXcmjZLKZGCTwuGvxbORwOPWpFQwuLZekkVoMdgBPulM+eDpgf2CzVM6x1SpizJMvwgiej2zQ6ih7AAAgAElEQVQ+2h9x/2gqZ7UbBdR5vqTlSbnsBzsDPnw6xNBULrZKJFGEG0HPDrl3OOZnj3vsjx1qBYNzjQJpkhALmHiCvYHFzx8POJlKZUqnahAG0i0+jGJcEXEyC5h6kXTOL5u4UUIkpCGjGydYQTxn82QjwQkTwjQjCBOGdsjzgbOo+5FgEkS4ScLUjzm2AnZ61qJu+TFWmBAmAisIObZCdrov6l6UYHsJURQxtAIOZwG/fCTPEwBDJ+Bo6OJGEQdDjyd9h5886J2p7w58xl7E457Nbs/mg50hAztYzPk+OLY4sDI+fDrgcU/e/6ffYS9KOBg57AxcvjyY8OXRhAfH1mIk5nRO92Dic/dwwv2X6tlLmaBOEPO0b/GkZ/G4a58ZqVEUhd7M54MnI377bMiHu6Ozvhnz9/Gb3RG/2x0vXj+8UO0OnYCP9sZ8sjfhF48HHE+9V67//GDKFwdTfvqwz/ORc6ZuBZLh/exgyq92hnxxOFl8BsjbkyzLuHts8YvHQ3728AQneNlwTTbJ/5+7x/zdkz4fPumfqZ/u1d/eP+HvHv899Qddfvbw99d/8qDLz19TNzSVOM34aG/Ib58Nedq3vvL6/6U53v/29TVA/Xr9g9apnPftrSVurNdekRIs1/L8h+9e4H/91jb/4bsXXpESX2iXef9qhzc36/zRlc5CQny61htFLi2X6VRkNEqnmls86ExdZa1eZKWap5w3WK0VKOeNxcGzUTa50JazinldwzQ0jifB4tB2Y73Gty4uc6lVQdE0cobGTt9lYAeLrNgf3ljh+mqNMJEznQ97FvtjZ+F0/Gc3VrnarkmGM4MvDiaL+g+vd/j3723w1noTNxCoqsrTnsPO0KJdzfNn11f4d7dWeOtckzBOQVW4N5c510sm37rQ4lvnG1xplwnmHfvnE4eTmQSYf3J9le9f73BzLqHJspS9kUt36mPqKu9tL/Fnb6zx7oUlTE1DpILHPZve/IG9Vjb40c0Vrq5WqRQMNE1h8hKAXK0V+PbFFpfbVUo5nTRNsfyY2fwHq1XOcWWlSqdkkjM01CzFDWJGM29RvzzPaazkDHnwiAX3ji15MC/nZI5jLUdj7vociZRf703mID3HleUyG408rXIByEhSFgfz1VqBS3NX3041TygEXhjzxf7sDIhfreRZqRXww4REpDw6nC328BvnW2wvlVmuFYhERiQEu31rAY6urdY5v1xhvV4kQ8qoXpZ6d2p5ttsV1it5DFWCpL4TLDrr1YJBc57bpyqy7gQxsZAAs2hqVIvSLv4UxDu+WDDxRVOjNq+bmiLrYcrBWB58Tg3HynmdvK7gBQl2mHK/Zy/2eL1ZoJjXKBsabpTghQkfPZ8yceQebjQKkGVUTI2pH+FFMXcPXzRCtltFDFWhbOrMArmHT3rWYg9vrdcpmTolDZxImnw8G9qLPTy3VKaQMyjoCmEi6/vjF3tYKegUcwa6opClLEynTkG8rsnMI5GkpMz3yIvOuIKrmsJ8ugg/kXPE05caIdKsSf4wW/PO89P54dHQNJaKJkqmogAjL2TqxXx+MFtIPFulHKQKqgJjN2QaRHywIwFkrWCyUpUyQV2Vbs2uH/P58xf3YadqEkWCvKkxC2K8QLDbe9HouLAkI5IMXcXyZQxKb+JzYnmYukqzlJtnTyvYoezO7wxeAMC8qYKiIqKEMAbLi8+A9FhkpJmUYQYRjN0I23sR0h6LDFVVEKl0Tj8ce3QtX+aWLvZQI4kFsYDjmcfBxKU7lkZz0jxFw3IjogROJg7dqc9o9mKkoFXKMXNC/Cjh4ZHFzsDhUdee38cmG/UCU0/OCd47mvH50YyP9yYvQHwjx9j2CZOYhyczvjyw+PnjIRMnolE2eWerThBlBEHEwcRjp+/xy50xAztYzBqfNm+6TkhvFvLo2GbkyvtURjClWE5Iz43oOxFPuxZDV74HTVPJUAjilJmX0HcinvWdlz6D00gHhTgWeIkgyuRhcsESG3OW0EsYeRFjV35G5bzOO9tLrNVL5A3p/n009RlZPl6U0Cib/OnNFbZaBco5fZ6ZGxLECbqmUC+Z3DlXZ7Wek3O0Ak4sDycMiYWgXc3zvSvLXO2UqJXks8YOIhIh/Q9a5TzfudDkSrtEwVAo5hWEEKQpi/q723Va9RyayhxgJ4RxyoktfR9+cHWZtWaJogkmUhVjKDIXNRYp7223WG+VKUsTd1w/ozcL+MXOmFikvLu1RLWUQ8mk5N32MmZuzKOugxcnrDVKmKZ0Q46AIATXFzzsOsz8SCqW5uOFQQpDJ+OTZxP+r0+P8aKElXqBOJUy5CCFkS344PGA//jxEV6UsFYrLqSobgj7fY+/vXfM//HZETM/QkFB01QUFZIETkYOf/PFIZ/ujRaziWmmIBLwYxhaHv/x0yN+uztcfM+yFFKRkSQQBhGf7s8WACoRKV4kx1UsL8PxIz7eny4AWBAJgkwQZwLLS0lEyuFUPiMAXD/BjmPcMGbgJAydkKfHzqLuR4KR7TO0ffpWTN8JeD50F/ev5cfsT3xGLuyPfB73bO53J4vntBclPOl5fHkwZrfv8KRnczBxXxpbkgzp4yOLw7HPbt8+85w3NJnB+snzKQcjjyc9m6OJf0ZR5YUJv9oZ8Wzksj/1eD72ORi/AJBxkvLlwYSP98f8bm/Ir3dGfLI3XACwWKQ8ObF4dDzjt3sDfvtszE8f9RYALhYp+yOPo7HPb54N+ejZiP/8+dECxJ/O8R4MfT55PuJJ1+LT/cki0kqZv4+hE/Lx3oinfYvfPZ9yMjsLgkdOyL3DGQ8OZ3x2ODvTyFMUWb97OOPB0Yt6NtcLn9a/3J9y93DCx/vT117/8Nhip2/z5bF1pm7oKqYGT3oOe0OP/3rvVRD+r319DVC/Xv/gZep/v6PYci3PjbXaa+dcTyXE/8vbG/zF25uvsLRrjSJ/8fY53r/S4U+urtCpFc44xEmmcpUbq1VurjVYqeUWWZQgO+s31+q0qjmurVZpFs8C3AvtCm9vNViryly46ksAt5zXubZa4xsXmrQqJldWqosonpfrtzZrmKpGvWjghHIW8rR+uVPl2xeXKJg61YKBGyW4frIAyN+53OFbF5ugqGgqC5nzaf1HN9f4o6stdEVHURQmdrSQIS/X8vzb2+t85/IS1bwJioIbpXRnAVGS0iib/PBah/e2mrTrOVRNJcoy7h45OEFCXld5/2qHb2w1aVZy8x/IeAEg6yWTb51vcWu9Squak/KuJOOjfQlu2tU8P7y2wrvnG5xrF0lS2UX+xe6Y/ixY1N+/3OL6WgUxd1990rM5mXm0q3l+cG2FP7ve4Y21GmGcoioqzwcu+xNnUf/R9Q7vbi0RxAmRSLjXnXAy82iUTf6ntzb4wfUOt1YbRHFKhpQ4njYJ/uT6Kj+6vcI755ZQVZUsS3nYt9jr2wBcWK7w57dXubEqXVUVMp6PXZ7P6+eWSvzJtQ7X5jNkuq4wcV5ItVfrRb53pc2FlSr1iozHCcMXmcGtcp73tpe42q6yVM6jGQpRJHjSlwfzVjnPN7ab3OjUpGOuLudnTpn4VjnPe1sN7qw3ONcugwJpmvHxngQ/p4ZjN1drXFmtoWQZZBkP5iC8Xc3z/SvLvLvZ5MZ6jTTJUFWV3b7D/sSRRmjXO9zeqHNzzjApKGcaId883+LOZoPbmw00Tf5AnjZCQMrh395ucWWlScEAXVXo2eGCfWuVc9zZbHKl3aCcly6qdvCyXN7kxnqDC80qxZwEiX4gFo2QomlwsV1hs1kihzTxCGI5M34K0tfqRerlHEVTQYiMKBLcfakRstks0KiaLBU1hEgJUsEXBxKEn5qu1UsGyxWDOMlkM+dE3qetSp6LyyValRydSo5gHqWzP5Yz8fWSyYV2iVrOoFEwCBOIs5SDifcCxK/VqRdMqnmNMAYnjnk8sBjOD5+VgkGzkqdRko2cIEnYeelwd5o9XS4Y6PNGxvEsoDc9ZXE1Vqp5mtUCOUPOFTt+vJBim/OICQ05chAnglkQM5zfp7WCQauUQ1FUNB3CJCGIBc/me9wq52hXcmSqgqGDFyS4YcJv96YLkN6p5tF0DdMEyxdMg5DfPJ83myp5VqomqqpQyCtMfcHMDxafQb1k0q4YJLFAUWHiCvwg5uHRi0bIZq1IlkpH0amfESWCvaH8DEACk9MoFy+Sxl8HI2dxeNQ0FcPU0ZSMIIahE7I7tBeHT0D6BmQZUQTdic/u2GU8v48VQFMUwiQiTOBo4rIzchbPgqKpzUG6jxPA457F4RR6i7rMLnS9mCCBna7N3cMZO93Z4nuyVisSxCkiQc4CjyXTHiUpF9plbq42SdNM7pETsDd2+PxgDMC3L7a5s9EkRRpNWX5Mz5JsUZSkfPtimzc2m8RRSiqgNwvpzXy+PJb//w9vrPKjmysUcjqkGRNXgvnf7U2IRcq/vbXBn19foV7RyWsQJuCHMV8cSJbs2xfbfGN7iUJOxdQly5qk0J1Ix94L7TI3Og00TXI4QQqhEBxMHA7G7vy3ukm5qGMqEGQwDUJ25yMPm80iF1tV8jkNHQiBE9vnYGhj+zHVgkmrVmJu0E4MHE99fr03YKdrSSl9vUjJnEffZbA7tPnwWY/9gSOZ6FKBknnayIIPd4f84kmX/YGDrsm5VQXwU+g7GX9zv8f/9uFz9scOpqayXs9TzCskgBXA50cj/veP9unOfAxNZbWWJ2cohBkcTCN+/uiE//TJoTQF0jSWK3kMXV7fdwM+fj7m/tF48R1fyufIRCrf29jjQXe6eEaUCybNQg4RJwhgOA24358ysl80iRqlPEEkvRz605Ddgc3x2J0bHmmYKtg+RAkMrIDuNFjc3wrSX+TY8Qjn9f4s4PilJpWhw4Fl48eyUTuyQh7PWWJZV3g2tHHCiO5UNrk+fj5ZAMiu5fHkyML1Qva6DkcT5xWW+JO9CYNZwJOTKUdjj189GS1A/MyPeNLz6DnSsG9g+3yx9wJAelFC3w4YOz47vRljL+LhicP+xF3UT6YeIzfk6cmMg5HH3a8oRfxYYAcRPdtn7MccTRws78VIjBdKpvnByYx7PYsvDseL5zxIkD2wfB71bR4ObO4eTV6pn0xc7ndtHvZtPtobcvAVgOn4MQdjj92RzYOjKSenLPGcRg6jFMsTHE08vjya8JMHvVdY2H/NS/vxj3/8L/0aXll//dd//eO//Mu//Jd+Gb937e3tsb29/S/9Mv7VrYKpUS+ai0Dzl5emKnSqec63Smy1SixXz2ZDmbrK1lKZa6sVbq/XuLpaO6PHL5gam40imqqwvVRitZ7nYruy+BumrtKpFvCThKVijmbZ5M2NxuK1aKpCs5hj5EWUcxqtUp4/utxeWIRrqoKGwrORLYOfTYM/vrJ8RuqcpBk7A4dyTqdWMPje3CBKU+UD3w0FD05mlPMGFVPj/asrcuZLVSiYGnYgeNSzqBdzFHKS2d14adY3SjIe920Khk69lOOtzQabS8WFU1siMr44nODHGWVTZ6NZ4lKnzLh3xJVLF8gyuH9kMQ0SyjmDWlHG5CyVc9SLJuW8wfOBy9CNqRVNyjmNS50yKzXp5LpSK3I48jmc+DTLOXKaxvnlEpvNEs1yjvVGCT8S3DuyKBZ0IiG4vV5nu1WmWZaxJmQpH+5OMExpw//OZoOLneri+iiK+XB3Qt7U8cOEO+t1LnQqc7fTMtMg4JODGZqqEoQp7201uLRSpZTX2WyU8KOYL45npJlCKDJWKgVurNcp53VW60WESLnftYiElCqXcga31+vUigar9SIqSDddPyYQgpyucXu9sajrqsLeyGHmRITz2Io3NxrUSyYFQ8fU4dhymToxSSY78Lc3arSreQqGTiWv0Zu6nFgRiqISJTLyZ61RpGDotCsmlpvwtG+jaypelHBzvcrF5Qor1QKVgoEfCr44ttA0Oc/01maDC8svPsdEpHx2NEVVFWZ+yNsbDS50qpRyMm/TDhJ2hjaaquCECW+v17m0Uls4FvqRYHfoQZbhJilbS7L5kzM0RCo7+geTYJ4pKdj8Sj3NUk6mvpSzx4JaweT2RoNiTkeIFEXJ6NkBSSKIsgxNU7mz2aSSN5h58gB3NJqia9LoRlVU3tiUM5jpvPlhhwmuH6HOZ7pvbdZZaxSlm2imECaCvhVQNA0EKbc3anIPa3n8SDKXhxOPQk4nEIK3zzW5uFyhXc4TCYGWqexPXApzA4p3zzW52KlSMDTGXoxKRs/2KZs6QSx4d1PusaYoDLyYVAgGXkQ9pxMkKdeXy1xbr5NlUpaIyBg6IaYm78O1eYNNVSToFyJlaAdoqoKXCJrFHLc3GmiKQnfmk2YpQzvC0BQikVEvmtzaaGBoKkdTnzjJcGN5mBJZhqbq3NlsUCuaWF7I0IlJ5uBU0UDPNN48V6ddzZMkKUMrQmQZEz+RUU1qxvW1KpvNEm4YczD1yBLpbF4t6PiJ4Ppale1WGRWVJ30bhMLAjSkXdLwo4fZGje1WmZmXcO/EQlUkOMqZCkGa8u5Wk81miYETcu/Ekk0iOyZvghelvLVR50KnSncWcO/YwjAUZk5MyZR78NZ6jUsrNfqzgM8PLTIyZn4yZ/QFN5bLXFtvMHEi7p9YhEIw8WM0NUOkGW+uVbm23sCPBI9ObAbz/OsslU6lW60Kt9blZ7Q7cHk+dPBFShhnRDEsVQu8udmgaOrcO5rxZGgTxSlukiEygaFpvHWuSaVg8LRvc//EIk4EXgiqkqJnGne2GiyVcxxNHT7emxAkgqmfkaQCLxS8da7Bar3AzAn4+aMBUZJihZDXIUkV3tissd4sMpj6/HJnTJql2DEYeoYbJby5UWO9WaJnBXywMyJMEuwIqnmNOE24tlrlwnIFL0z4cGeIFybYCeQM2Sy5vVHnykqVVKR8sj8liWPsRBqkxWnKe+ebXF2tkWUpX3YtKcVNQM9SRJrx9mad6+sNbD9id2xjWTEhkMXS2fitjTq3N5ukIuX51MGyIsIMlFSaCb6xWuWNc0uMnZCeHTB05WH8lAG+tFrlzkaD7jRg6sf0HfkdSASEUcKFTpmba3W6s5CZn9B1IlLACTMsN2SlmufGep2eFcnviStBoBtlTAKPZi7H7c0GhxOfaRgxsBJpfBaBFYY0CyY31mvsDVyOpw7TICPJYOKndKcOZVPnykqVZ32HvZGFFYGfwNCJ6U09zrUKbC6VeXhsca87I4ohTKUUOadrbC5Jx+HezOfzgwlhDFEGiUjIGwYXO2UqeYPDscfv9sbECYSZVPTEImO5kqNVyXE88flgZ4wAvBSyNAEUbm7UMHWNnb7Fh08G+BEEAhQ1RVFUbq3L+uHY4RcPe9hBhhcjJeu6wc31KoamcjB0+M9fHjF1BU4CBR30TOHGRpW8oXM0cvmbBydMvJBZCCVTJUozrq5KQ7z9kcPfPuoxtD0GLqianJO+tVFjqZzncOLxk/tdBjOPo1mMqYMnEt5cb7BcLdCzfX7yoM/R1GV/FKJrUq79zmaD1UaRk5nHzx4N2RtYPBv5ZJk0BLveqXCuVWbkhPz0YZ9PdyfcPbGIkpggynjrXI1zrTJTL+LLgwm/fTbh88MZSZLghSmrzQJXOtU5APb5bH/M54dTgljgRRmdWo6rKzXcSErD7x9Pedi1iUVKEGWUcwY312vEaYaSwWcHY744nnA09nHCFDK4tV4nzTJ0TWXkhnyyN2RnaBHG0sfj5ob8LdcUhbzx6ln7v7f1V3/1Vyc//vGP//p1ta8B6j9hfQ1Q/3nWKVArmNrC7e3lZeoylLleen149SmIWasX2G6VX8mnqhYMLrQqrDcL3Fqrv8LiVgsGV5YrnGuV+NaF1itMcClnoKkazbJkiu+ca5x5HQVTR1NVOWe0Vn+1bmh4saBeMrncqfLdS+0zYN3UVHqWTzmnsdEo8v2rnTMZWqamYQUxpqaw2ijy5mZDsnHzvSrMD8teKNhslNhsFrnaqTLoHrK9vU1O15h5IZO5q2C7bHJ9tUa9ZKKpMjMwSQRdO2ClUqBU0HlrUx6YNFWhWjAomhq7Q5tORQZ0f+P80gKEF0wNIeDZ2KFVyVEydL55oUVn/hpNXWXmJzzsWTQKOQqmtgDhp/WhE/PF4ZRKwcDUVL57eZlzc5dRU1exfMHdoxmVnEnOVHn/6sqZeprB3SPJFCyVTW6uN9hqFRfMv6mp3D+a4kcZq7Ucl9olzrVKi7DvnK7x4MSmZ8fUC+bc4KW8CPOu5U0en9g8H78K0qVTYoH+LGSn79IoSSbp6kqFrVaZetGkUytgBQkPjm1qRQORZdxeq3N++UU9ESmfHEjHbJFlfGNrie22dCLtVPNM3JCP9icUDB0hMr59YYkLy5WFRHTmRXx6MENVNEQGN1ZrXFmpzq8vECWCBz0H5vb+a40S1+cNn061gKoqPBs5eGGKqiqUTJ0bazXJLFZlJMjhyKXvxnMJjsLNNZlV2KkWqJdM9odStqWqigQvqxXOLZXoVAus1PMcjWSUTU7X8CPBpZUSl1eqnGuW2Fgq8OzggK6vkzM0wlhwYw7S1xslLi6XiWLBl8f24jvxxnqNS50qnVqBy50KmZLx+YGFoasEYcI3t5fYbldkdvFyhUAk3D+20RQIkpR3NyVIP31GJFnCoxNbzmxnGZeWK1xfrUn2JW+QkfJ87JGmEGcZnWqBG2t16TabM9AUOJj4uKEgSlNA485mg6VKjlrOQNcyjmeBnD/MIE0y3tysy7zJnEE5p3DshEzdmEyRh//bGzU2myXZXMprTLyQk6lPpkCUCm6t12Xd1CnkNWw/4mTiYxiyEXIKIFdrRVQtww4iDscBeVOC8BtrFS60K7TKeUxTI8ky9gYOuq5gexFvrtW4ulanVc6TMzRikXEwcSjoGpYXcWulyrX1+iLWyI9TjqYuRU3FDmNudSrc2GiQ1zX6diDn99yA/ByE31opc2OjsXgOJrFg7ErDskCkNIp53txsUM7p9CyfNEmZSnRHmKRoqsbb55rkDPn3yTLG87k7X8iZyHe3l2iUcozdEC9M8IJYmhGlKUms8M72Es1yDtsPGXkJURLjRSlokAm4c65Bu5rHj6QDre9H2CFoGaRayp3NOueWSuQMlScDGxELRn5K3pSuxjfXJUjXFZXHfRuRJEyClFxOvsc3N2U9ijOeDh2iUDANBKYOQZxwY6XKheUqIzfiQc8mS1NmvkDTMrww5tZqjYudKn074vPDKakQWGGGoWa4oeBqu8S19QbdWcCXxzOESJl6ApElWEHC9bb8DIZ2xMOeTRTH2EGKSKV79fVOhdubTZxQsNN3mXoRbihQSInTjCudsmRvXxqxCGKIBKQItppl3jm/xHIlz07XYXdoEwukcVMq2GiVeO98i9VakeNJwKPelCSRICxLBaqq8/7VZW6u1bD8iN3uVF4PaFlGJafz/WsdVmoFhBA8Pp4RpyAAJQVVzfjelQ5FU+ZIPzmxWbgAZGAY8MdXlikZGrafsNOTxnoZEMWQpDHvX+uwVisymEXs9x2iOZsVhRl+kvD+lWWqeYOdgcdgFi7+vhWkjD1fNr5NnSc9F8uJSeZ/vzeL2BtYfO9ah3Ylx8fPxzh+QpRJkPioa3E89fjmhSWqeYPf7Y1x/YRgXn98PONk7PHO+SY5TeWT5yP8SBCmcq78ZOJi+xE31+sYCvzsYU+acyEbqcczBy+IubJaIxUpv94dEccZYQZ6Jt2Rc5r8vXC8mA92xoShIMiQYz9RTMXUaNcKDK2AXz4Z4geCEFDSlCBJ2KjnadcKjO2QX+2M8LwYV8jrvThkrVpgY6nEcBbw88cDxtMYPwNFZCRpwrl6kc1Wmd7E45dPhwwdj1kEybzBsVEvcWWlysnU56NnYw4mFmMvI4xSUDM2m2WurlbpzwLuHk7ZHc4Yuil+mCIQKGjcOVenZ4V8cTjjYW9KbxoSxilSdKHxxmadiRtx79ji0+cjDsYeU1+QaSlKonBnq0GQCB6fzPjl45E0Q7RiMkUQRRlvnquTIeW7v3oyYqc75WgSoukZiRDcWq9RzBk4QczfPR6y03c4mgQYBrhRwvW1KrWCSZSkfLA7ZLdn8aTvYupghTFvrNXlmetrgPrPs74GqF+vf+r6QzLkv4/FBSjlpdvc68KVTyN3tpaKXFmpvgKA/1C9YGqcXypzoV3irZfCw1/+vy+1K2y3y3z7QvsVgLy4vlPm7XNNttvlV1jmc40S7WqOS8slLrYrrDeLHOw/Z3t7ezHLa8wlSueWylxdfTFPrKkKy3PguVzNcald5tbGWZBdL8q4jnopx9WV6mtBuBPGVHMam0slvnm+9XtB+PprQHhe1xjYISVTY6tV5k+vr7y2XslrnG+/Wi+YOlkm48bXG2WudMpceIlJL+ZknEmQCFZrRVbqsllx+hrzhkYcz0F6tUAp/wKkn34GBeMUpBdfAemlnE5O03g2smmWJav6nYvtMyBeV17PxJ+C9CTN2Ok7lAsG9ZeY+NPPyA0FD0+Z+LzG9+ZM/Gk9SjIedWcYmkq7nOPd8y3WG4UF025qKo97FiKV88e31qusN16A+Fre5HDsMfYizi2V6VQKXOqUWSrnFk2i3synNws43y5TzRtyfrwm/49GKSeNOoYO680CpqZyY1Ue3E/zQINYcO9kxkqjgIrCnc0G2y0JwtuVPE92ntJLCjSKORQF3ttaWlxfL5o4geBB16JVNjE0lW9caLHdKi8+A8dP+PxoRjlvomsaf3S5vWhkFEyNIEq5dzKVUldVYa1R5OqK/C4UTI1EwL0Ti1hkmIZGu5zn6kpVzhqXc+QNnZ2egx0JSl9RIzTLOVYbRY7GPidWSKucp2CcVRus1Io8H/rsjz3qc0B3bVU2MprlHKv1Et1JwKOuTcnUyFJ4Y63OxU5loTYYuxGfHczIGxp+KLi9VuPyapV2RapHQiH4dH+GaWg4UcI7G/WF2jWk1JMAACAASURBVOB8q4IfJ3x5aGHq0jjsUqvM1TXZqFivF4njhHsnM0QmweparciNudpgs1GSLNnhDCeUDtHVQo43N6TaYL1RRFMz7h3PsHxBnGbomgTpy7U8l9sVNE3hcOLihDK3OYplXMdqo8CldoVqyaQ78+nbMSgqXpxwa96ouNSu0K7lGVgBB9NIZujGCVdXK1zqVKnmDJbLBidWwPHURzdU4izj2mqFq6s1zjVKVPM6A8vn2TDA0DW8SHBlRT4TV2pFVDVj5oUcTQNKhoYTJlxplxYgXVNh4EQM3BAlhShLudoqcW29Ln9jDAMnEBxOXfLzuf/L7RJvnGtSLRjkdR0vyjiaOuQUGUdyqVXiztaSbGZGCWEUc2z56IoEmGuNEm9vNSnMQbgfCYZOgJqBFwsqpsE75yWAGTohUZJJkJ9J80BF0fjmhRbVeRSYE6ZM3YBUSAYyEfDe+Ra1gsHe0GUaxszcCJHKkQMnSvjGdotmKcfRxGfgBThehBdDmqZ4say3KtJ5/tk0JAxj5hgfN0l4Z6vJuaUSuqbwxckUP4zxhawHScI723PDQRTudi2iWLKwOV3Ond/aqHFttYauqjzs2QRJiBdDyZT1m+tV3tpaIgOejmwcPyIUYKoypu3qSoU/utIhJeNxf4Y9Z0nJpLncG+tV/vjqCooKTwZTLFcsQGaSJVxplfjBjTWKpsbD/oSRLaN7BBBGEZdaJX5wbRUrink+nDELJYLNgCRK2JobK469mMPRFGuuPE8By4lpVQ3+9Poali84HFmL6+MUTkY+jbLOty8uM/IShq7H2Jsb6WWwP/Qw1ZTvXF7meBbiBCFjX9ajFI6HHpBxa6PJp7tHqIaCHcn/20/gcVfKqG9vNtgduERJiBVJFnfoJtw/GeOFKRc7FZ70bII4wp6zrEM34VF/jB+kbLVLMgs7CHETef3EF+z1HVAV1upFnvQsJq6PE8vr7VDK6VVFYbVe5N7JjOEsIEiZz3hnHE98UBXa1Tz3T6YcjwN8IZ2sSTNmYcxGXWYjf7Y34mnXxUvl3hBnDNyA1apJo5Tn7uGUx8cWjpCfnSrAjgJWKyZLlQKfPBvz5f6IWSxl6oiUqe9TyWtUCyYPTyw+edZn4kuWWk0yZr5PKadTKZjsDF1+s9tjagncDDQynCCkXTJZbZZ4dDLjv9w7wXIjZgnopIy8kEbBZKtdpmv5/KfPDulOHcYBZCJlFkoQf7FT5Wji8n9+csTRzGVgCTJSgjihWchzZa1KXte+Bqj/HOtrgPr1+u91/SEA/M8JkE+vXyrnqBaM17LMBVNjtVagUyvQnsukX75fS3mdraUS640iF9qV14LoU/b1UuefBsK3m2XOtUrc2fjHg/BSXufKcoWLnQrfvfiPr/8hKbimKqw3ilxfq3J5pcybG40zr/FlkN6u/CGQbr4WpFcLBoaq0Szlub76av3/Kya+UTK58homvmDqKCgU/9/27jy2sus+7Pj3vHfvffftK9/CfRkOZzibNKJHW2TLlmRL8aLaRQAZsB0HBdLFSey2QJsUDVoURWEgQdEUSW0YTtoETWukToq6gROncS2kCFzHslxHGo1GMxrNQg735e37O/3jPL4hh+QMVS1DVr8PMBjy/t5980j+OOTvnN85x2fWdJ7oj/YKYDBFesS18XgUY0lzTu3WM+j8jpeBmJ96q01/1KUv4m5rhzefG5e1cp2wzyEWsHloPLWtHT7md5hdrxBwvMQDPp6czppW0c3PgWNxY72Cz6PoC7t85ERuW/z61at0AgkirkUy6OPxYxliwVtfJ5/l4dpqGZ/lIRP177i/09FcXy0T9ltkwjvvd7werq6UaHY6DKeCTGejZKJud/Mb062wkK9SbrYYSgQZjgcZ6wv2vu+CPptCtc5GtUlul26EiN8m6LP2HMgwcS9XV4tkI34Cvu0DGUGf+Rpe6u58GrQtfupoetuSgEq9xSvzBWIBB5/l4ZFut8FmvFRrc/6m6TZw7Z3dBs0WvDKfx/JaRFyLmdEUI6lgbyCjo+H8XB6lILVLN4JC8ep8gWrTrLHORH3bBjL8tsWlxRLrlQaZiEsi6GMsbZYMBF2LdNjPy7MbLBQa9IV8hAMOY30mH4OuRS4a4NpKmdeXyySDDo7Hw8mBGKN9IYLdInqpUOPCfJF40EF5FGeH4kxmzZKBkVSYVscU6QGfRaMN50YSvSJ9JBVitdLgxzfM7rH1luaBoTjH+qO9Ir7ebnN+No9SXurtDtlIgJODpkgfS4VRqsPFuQKNVhvl9RC+rUhvaTOTUmsr2lrjWHbvXN6BuCmCL9zMk6+bWcpyvcPZ4Ti5uJ/RRAifz8vl5QLFagfd3ZX3zLAZ7DnSF8a1Pby+WiJfaQOKZkdzasi0sx9Nh8nEfNxYN0sKPB5TAB7LhZkeiDGeCmF74UahzHKphWWZtaJT2TAnB+OMp0IEfB5uFs0ggc82m9+NJwOcGUkwGPebmb9yjZVCtxW73mIk5uf0SIJc1E+13ma5VGWj0sJnmXPGh6MuJ4cSRF2HelMzX6hSLDfx27BRa9Ef9nHfSJKwz6bR6rBabrBabOAoKHd3zn9gNEnQsXo77S5u1LAUFMptoj6bR46mCftsVkt1ao02y4UGXg9Uah0cr4cPHMuQDruslBqU6jXy5TaWMmv6PErx+PEs6bDLQr7GarlEvmaKxFYDap02P3UkTX8swHq5yXw+T7FmBkXbLai3GzxxvJ8TuSiVRofrKxuUu0sXW22otho8dTzHAyMJ8Cpeu7lOtbtssAnkKzWePtnP/cNxSo0WF67n2Vz52ALWCmWeOtHPWDJEp93h5Rt5NlcdtoGNcoUPTec4njVLCl67mafRufX8y4USD48nqBeWicdTzC5Xevc3NawWyzw0FiMZDlBstFlaq/XilSYsbhR4YDhKPBigUG+ztlHrvb5SHW6uFTk7HCXsuqxXmpTKDTMDDqxWW7y+uM7xbAjXtlkqN6jXmnS3+2Ct2ub6ap7j2RAevMzlK3TaHRqmu5X1aotrK3mO9AVpdxTXV8vQNrO8rQ4s5eu8MpdnMu2n2VFcXCxCS9MEOhpWi00uLhTpizpEXJu/nivgaXXMLK+GtWqH2Y0KyZCN37H48Y08qqVpdOMrlQ5z6xUaTQjYHl64nqfR0nQA2wMbNbP0Y6VQx28pfnRtg1JTmwGOtmkl36g30ZhNAb9/aZVas0OtY3Kj3IAb6xU6WuMB/uLiMqWSmaVud8ws/vW1Eh6vgrbm+deWWCvWyDeh2TIz4XOFMl7Lw2Q6TNi99TPxoJIC9W0mBao4yDZn4jYLktvz9a0W2e90Ef5W43drBd8sEJIh366v8a0W6e90/G4z8Zs7lR5Jh5jOmTVpWz8PXo8iF/NzNBNmIh1iYpeBCtMKG2EoGdi1HT7WLY6HUwHOjSV3DBTEgg7Hs6al9PGpdHdG5JaI3+ZYJsJYX5gPTmV2xBdmr3P/9BTZWICzwwmOZMLbBmQifpupO9wf8Fkkgj5ye9wfdC1Gk0G8SnE0HSYedHoztJuf47utae+PBbC8HgZ36UaAuw9kmMLSIhHafSAj7LOpNTvEAg5HMuEdAxEB2zLdBj4z6LRnt4HfYmyXbgS/7aXUaONzzA7MD4zsXDKgtUYrxUAsuKMbwWd5KdebbFSbpEIu2YjZJGtzoMC1vdDpsFCskQiaMxQfGk/2BgqCrkUs4HBttURf2CVge3lssq/XreBYHsKuzRsrJSJBm1jA4cnpTC9uCnGLK6tmbV8i6OPDJ3K9JQmbRfilpQJh1yHqWnzoeG5bt0Gz1eGVm3kCPpuwz8tHTvVvW/dvKS8XF4t4PIp02OHkYGJbkR5ybF5bKLBRqjCRiXI0Hdq2ZCDqOrw0u0Gp3mIwHmCiu6Rgc8lAzO/w8twGq+UGA7EAibCP0VSI4WQQv+MlHfLz+mKZm/kq6bCfgGNxLGva5TeL+MuL5jijWNAhYFtm3X+3iB9KBFncqPHaUpFEyEWhuG8wztFuN8B4X4SVjRoXFwuE/Ta642VmJMHRnIlPZaOU603+etbsC9BsK6YypoDdHCCr1Ju8PGfOOq01O+Rifu4bTpAK+ziejaB1h4vzG3Qw3R2WZXF2OEEq4uNIX5iObvPa/AYNbXaZbnZgZiRBOuoykQpheTUX59eptDXt7kzwAyNmpn0sGcKjNBfm1ik0TCFQaDS5bzjWm2n3O4rLCxsU6h2UB+q6w+nBKJPZCBN9IVodzbWlWzOZlUaTk4MRjuWiTKbDWJaXm8t5inWN5TWb5xzLhjgxGGckEaTZUcyurFNsgKVMG+Z4ys/ZsRST6TAoD3MrecoNjc9jWrWHYy7vm+jjaDqCVooLs+vUO93Nv1otchEfD0+mGUuFuLFqdmndPECk3WqTCvv48Ml+RvpCzK2WuLJa7R2/0mx0iDgWT58eYCId4sZKiesr1d4scLMBlsfDuVSDD85MM79e5dr65pFvpgh1leLzj00wnHA5v5DvrfUFKDdBtTt87tFx+kI219YqvbW+m3HaLT776Dh+12K+WGP5tvhiocynz43geC3ytQaLxVvxYl2zWCjz3PuG6QC1doulovn3NVCoa2ZXC3zsVJZCo4PtaJaK5tigDqYIfm1hnSePZ1ittPDabdbKZmf4DrBWaXF9pcDZoSj5ehvLUayXWr1W69VSk6vLeaZzYdarLbPWvdrq3V+utJgvFBlKBlgvt3Atc+xZU5sBglq9xXyxxEDcT77apt1p0miZwYE2UCrVubxSJBd2WSya3fTrre4sLlCrtbmytkHMb7NQrFFttqm2zL/tVVCstnlteQOfbbFWMjPk9ZbJfcsDxWqHK8sbOJaHM0OJXScyDhIpUN9mUqCKw0Ty9c17p4v4d3oQwLE8ZpbOtXb9AbU5Sxfx2+/YQEPEb9Mf82+b2dxv/Pq1a8ycmmIg7qc/5t9zzfle93s9ilTYd8f7za68YTJRl+FkcNc163da0363boR3Y0nA0bQZZLhjt8Eecb/jZSIVYjITYmZk9yUDI8kQx7K7dyM4lofhRIi+iI+JvnBvdnMz38xASIBMxGUkGeD+XQYK0hE//dEAuaifmdEkx/uj2+LxoI9sxM9gLMCD4zvj0YBDJuwyGDf7Btwe3+w2SITM7uy3F/lBx6LeapMIuJwciPD+o5kd3Qhaa1CKgdjuSwbCPpvZhXmmhvp3LBmI+G2GEgE2qi2GEwFifh8zo4ne94yZSfbxxkqZaMAmbNs8OX1rtt/veIn5ba6ulkiFfIRcm8en0tuWHMQDNtfWSiRDLvHgziI+YHt5baHQ3VzP5qOnB3tLBhzLg8+2uLxUMrvDBmyeOdVPJnqriLc9Hl5dKGJ7PSSDPj5wNLNtcz7H6+X8zQJousfUJBntCxL0WabjJ+hycbFItdlhIBFkNBFgtM+s6/c7XrKRAC/P5SnW2vTHAgwnzfdbf8zfi786n2e9Yor8ZNjdXsSH/fxkdp2lYp10xEfEdZjKhDmSCXdn2oO8tlBkLl8lEXTwW2a37SObm+8lQ7wyn2ehUCURcrC9Xk7kohzrN2vSj/RFeH21zNWVEkGf+bqeHUkw3V1vfTwbZS5f49pKkbDfQmsYTYW5bzhBJGAG0pZLdV5bKuDxmh3wQ67DAyNJ4iGHkwMxavUGFxYKOLaZSfMoxbmxFLm4n/eNpag3G1yaL5qdvTtmI7uHxs3eBOfGUzSaDS4vFM0xdEC52eJ9YwkmMxFODsRYzFe4tlzZsgN2m5FAi088cpoTA1GuLRWZW6+ZIggoNBo8MBrjA8dyjCT8/OTGMutVUyJ7gGqzycxonCdODJCN2Lz4xhLFLceYFusNzg5F+ejpIWKu4ifXltlSo9JotBiJB3juwTFifg8XZld7AwRgdg8fjQf47CMT+G0Pr8+bAYhevNohGrD55NkhgrbDzZU8+S3xUk2TjZpcjbouq+sFNrbGqx0cj+bsSJJEyCVfLrNe6x4RBJRqHWyPZipndmWvVEusd08za2GKaI9qMpYK4/psdKvWizc6Zia03qxxrD+O9nhQ7Xrv46tryNc6rJdKTPdHUcpLPAxrRdNK3sLMRK8WigzGw9Q7irjbolA1sTZmJnZ5vchIIkS9DamQYqPS6RXJxbrm4s01RpN+JrNRDjIpUN9m8gu/OEwkX8Vhc/XqVSbGx7Z1ArxZt3cS7OZeDzTc626Euy0ZuFs3gmkHDzAQ95ON7hwIcCwPuaifoURg14ECr0eRjriMpIIM3jbT/3bE71bk+x0v46kwk9kQZ4eTu3Yj3KlI31wy0Fm7wYdmju9YMgDmeKZTAzGGU0FmRhM7Bgr64wHODJrdzp+czjB62xnhqbBLLuInFzXdCrcX4amwS380wGB89yI+HvSRibgMJAO8/0ia00PxHd0IPstDNuLn7EicmbHkjiUJllcR9tkc749wejC+bclA0Gfj8Wi8yst42mxCs2Pdv+1lo9YmFXJJh13OjSV7ORnx2wzE/CwW6uRiPiI+h8cm+3oDT0HXIh1xu0W8Q9i2thXxJsd9XFo0R8hE/DYfPzOwrYiPBmwuLZWI+h2iflOEbxbpQdciGrB59WahN2j3ifsGekW6v3us0IWFImHXJh7w8fEzg72ZeDOI4PDSjQ08XmVm8qdzvSLe73gJ+xxevllAYYr4E/0xJjKh3kx7IuTy0vV16m1NKujjaDbKRHdzvmD3GLvzN/MU6k1ycZdczGU0dWsmfTO+UW3Q192p91i3gyUWdJjuj3JxMU++Wqcv7CMa8JFzGzx88gjJkI+TA1FemdsgX6mTCNr4bS/TuRjTA1FGUmbn4wtza+TLLSIBLz6vlzOD5kiyiXSEo5kw52fXKVZahF2FY3kZjpvi+Wg2Rjrk4/XFPBvVNgHLtLeOJIM8fKSPqVwMy/JyZWmVQh18gG1BMuTj/VMZJtMRlopN5tcKVFqmQPZ6TIfJ06dznBqMcTNf58ZCic0a14P5Nz75wCAzYwmqbXjj5gbdGrv7HIpP3Jfj/VMZ6Cguz92Ka0B54FP35XjyZD8Bn8Olm2tUtpzgYikPn7q/nw+fHMC2bK4ubWyLKw0/fTLLo5NpLI/FzeVi7/nBLEOZzkQ4N5EiFfAzu7KxrYhvd2AsEeDUYIy+cIiVjSKlLc/fakEyanEsHSMZclnfKFNq34o3W7BcrvNwd835QXWnAnX3n1pCCCGEEHexOZN2UOPxkLOjaHwz8ZBrEXJDe8Ydy0MqYIqEvaSj7q7ng28a7QvtKEy3Pv/Z0UT3DMudH+t+4g9OpPaMh1yLp07kqDRaBBxrx0x+yLV45tQA+WoD2+PZMVgRci0+fmaIhydqZhY17O6Yaf/A8SyTuQiFapNsxL/jczHT3bl3oVAjG3F3tOzPjKX41ZDL7EaVwZh/x+fq4SNpUiGXa2sVRhIBJrPbvxZnR5J84fFJ5os1cmGXI5nwtviD43188SnN3EaNgZjLsf7ts073DSf4zIOjLJVqpEMuU7ntz39qKM4Xnpjk/HyRTNjHRCa07Rz3qVyEv/nAAD+4vEbANbuxR91bOXckE+bzj4zyx+cXCDum4E4Ffb34QCLA3/vQEb72F1fw2Yqw7TAY8++If/X5y9iWh7DrMNJdbw4mv7701BS/+b1LeLQi7LfJhSq9+GQ2wi89OcmvfeciaAg6Xo733/oYZ8ZS/IOPHOfX/+wiHmVa67fm+2NTWX4V+FffvkCj1SHi2kxmIzTbHYI+i4/eP0wq6vJrf/IqpVqboN/iSPf+kGvx3LlRBqIOv/HnlynUzdFWw6kgzXaHgXiAX3ziKKcHQnz9f11htdIi6rfJRl3QitFUiC89dYzhRIA/fPEGa+UG6YhLMuyj1uhwZijEp8+NsFKs8ZeXFlkra/qiDkHXwbFsJjMRPn7/IK8s5HnxxhrrFchFbAKOjWOb4h6t+d7FRRqzRfIdSPm9uLaHsN/loYkUlge+9+oi1VqVMhDrFukR1+Hpk/30hR1+dG2N4nKVBuAo8FkW0ZCfJ6ezXJwv8OL1NerzBdaaZhbb5/WQjgV49uwAN9YqzG5U6dxcY7Fmzmr2OZAOBfnE2X5WSw0KtSZqdo357pfVZ0PQ9rJQqO34fjospEAVQgghhDig3uki3hThe/86uL/4nYv40dTecTBF1p1+kb5TEQ+myLq9MN3679+pSHcsc6TZnYr4Z07371nEO5aHDxzLMjOWAtjRFRFyLT55/zAPjadotjS5qH/boIhjefjImUFOjST2LOIfPpImFw3csUi/U3xmLMU/31LkXz3/wrb4Y1NZkiGXKytlxlNBpgdi2+KPTmYAtWcR/9hUli/7HV64vs5QLMBkJtwr0h3Lw2NHsySDLj+8ukY65DKRCfU2pQu5Fs+cGaY/HuLPLyySDPgYTgWI+s3nKB11+ZkHxzmai/FfX5zFtWwG4i657iz3QCLA3/7gUe4bifGfvn8dn8/Mag/GTT6N9oX4x8+c4C8nEvz3lxbx2RbJoM2RtBmomMxG+KcfO8XzFxf405cW8FteQkGbqW4+TQ/E+BfPnua7ry7y/IUF/D6bmN/h5EC097n9l586zXcvLPG9iwsEbLPD/v0jCRzLw8MTaf7Zsyf5L391g5fnNmi2Ndm4y9FMiFTIJTBscXpwlXanhVqtYHlthuJ+RlIhhuMhcpEAP762Tr5ap7ZaBgWDcbO8ZCoThQxcmCuwXmlQb5VoKxiMBcnGAmQjew+MHXRKa333R73LZmZm9AsvvHD3B94jzz//PI8//vi9fhlC7IvkqzhsJGfFYSL5Kg6b/5ecbbQ6exbx+33M3eKlWmvPgQCA9VKDfK1B1N2982FurbLnTPx+4leXS3sW+fuJX1oo7DmTX6q1eH25wHK+TjriMp6+tXfBUr7G6ytFypUmSkEs6DLeF+p9jEv5mmnTLplG5ljI5Wgm3BvI2IyXqy3QGr9rCvCDPnuqlPqR1npmt9i+ZlCVUk8Dv4GZef661vrLt8VVN/7TQAX4vNb6xf3cK4QQQgghhDi47jYTv5/HvNXZ/Lu15N9tJv6tztS/lZn8kGtxZigBQztjd1sG8Fbjh9GdMw1QSnmB3wKeAaaBTyulpm972DPAZPfPzwNfeRP3CiGEEEIIIYQQdy9QgXPAZa31Fa11A/gG8Oxtj3kW+D1t/G8gppTK7fNeIYQQQgghhBBiXwXqAHBjy/uz3Wv7ecx+7hVCCCGEEEIIIfa1BnW3Q+Ru31lpr8fs517zBEr9PKY9GKCklLq4j9d2r6SAlXv9IoTYJ8lXcdhIzorDRPJVHDaSs+IgGNkrsJ8CdZbtS3oHgZv7fIyzj3sB0Fp/Ddj1sNaDRin1wl67Tglx0Ei+isNGclYcJpKv4rCRnBUH3X5afH8ITCqlxpRSDvAc8K3bHvMt4HPKeAjIa63n93mvEEIIIYQQQghx9xlUrXVLKfULwHcwR8X8jtb6vFLq73TjXwW+jTli5jLmmJmfu9O978hHIoQQQgghhBDiUNvXOaha629jitCt17665W0NfGG/9/5/4FC0IgvRJfkqDhvJWXGYSL6Kw0ZyVhxoytSWQgghhBBCCCHEvbWfNahCCCGEEEIIIcQ7TgrUN0kp9bRS6qJS6rJS6pfv9esR7z1KqSGl1PeUUheUUueVUl/sXk8opf6HUupS9+/4lnt+pZuzF5VSH9ly/QGl1Evd2L9VSu12NJQQbwullFcp9WOl1B9335ecFQeSUiqmlPqmUurV7v+1D0u+ioNKKfX3u78PvKyU+s9KKVfyVRxmUqC+CUopL/BbwDPANPBppdT0vX1V4j2oBfxDrfVx4CHgC908/GXgu1rrSeC73ffpxp4DTgBPA/+um8sAX8GcPzzZ/fP0u/mBiPecLwIXtrwvOSsOqt8A/lRrfQw4g8lbyVdx4CilBoBfAma01icxm5I+h+SrOMSkQH1zzgGXtdZXtNYN4BvAs/f4NYn3GK31vNb6xe7bRcwvTgOYXPzd7sN+F/gb3befBb6hta5rrd/A7LZ9TimVAyJa6+93Nzr7vS33CPG2UkoNAh8Fvr7lsuSsOHCUUhHg/cBvA2itG1rrDSRfxcFlAX6llAUEgJtIvopDTArUN2cAuLHl/dnuNSHuCaXUKHA/8AMg0z1/mO7f6e7D9srbge7bt18X4p3wb4B/BHS2XJOcFQfROLAM/PtuS/rXlVJBJF/FAaS1ngN+HbgOzAN5rfWfIfkqDjEpUN+c3XrxZRtkcU8opULAHwJf0loX7vTQXa7pO1wX4m2llPoYsKS1/tF+b9nlmuSseLdYwFngK1rr+4Ey3fbIPUi+inumu7b0WWAM6AeCSqnP3OmWXa5JvooDRQrUN2cWGNry/iCmjUKId5VSysYUp7+vtf6j7uXFbosO3b+Xutf3ytvZ7tu3Xxfi7fYo8Aml1FXM0ogPKaX+I5Kz4mCaBWa11j/ovv9NTMEq+SoOoieBN7TWy1rrJvBHwCNIvopDTArUN+eHwKRSakwp5WAWmX/rHr8m8R7T3VXvt4ELWut/vSX0LeBnu2//LPDftlx/TinlU0qNYTY++Ktuy09RKfVQ9zk/t+UeId42Wutf0VoPaq1HMf9v/k+t9WeQnBUHkNZ6AbihlJrqXnoCeAXJV3EwXQceUkoFunn2BGZvCslXcWhZ9/oFHCZa65ZS6heA72B2SfsdrfX5e/yyxHvPo8BngZeUUv+ne+2fAF8G/kAp9bcwP7B+BkBrfV4p9QeYX7BawBe01u3ufX8X+A+AH/iT7h8h3i2Ss+Kg+kXg97uD0VeAn8MM6ku+igNFa/0DpdQ3gRcx+fdj4GtACMlXcUgps1GXEEIIIYQQQghxb0mLrxBCCCGEEEKIA0EKVCGEEEIIIYQQB4IUqEIIIYQQQgghDgQpUIUQQgghhBBCHAhSoAohhBBCCCGEOBCkQBVCCCGEEEIIcSBIgSqEEEIIIYQQ4kCQAlUIIYQQakGtFgAAAAxJREFUQgghxIHwfwG8wwr838GyMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c.plot_progress()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "defcb805",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[0.491, 0.3574, 0.2003, 0.0857, 0.0, 0.25, 0.2334, 0.2562, 0.2559, 0.3422, 0.3121, 0.3296, 0.3314, 0.4177, 0.3918, 0.4057, 0.4245, 0.4723, 0.458, 0.4599, 0.4764, 0.2445, 0.1441, 0.101, 0.1761, 0.2559, 0.0817, 0.0572, 0.0156, 0.0, 0.1246, 0.0786, 0.0769, 0.0799, 0.18, 0.146, 0.1573, 0.1772, 0.2269, 0.2084, 0.2108, 0.2283, 0.1847, 0.1376, 0.1711, 0.2533, 0.3314, 0.1084, 0.1067, 0.0819, 0.0799, 0.0804, 0.0479, 0.028, 0.0, 0.1083, 0.069, 0.0825, 0.0982, 0.1504, 0.1288, 0.1334, 0.1488, 0.1318, 0.1741, 0.259, 0.347, 0.4245, 0.1911, 0.1987, 0.1766, 0.1772, 0.1232, 0.1281, 0.1053, 0.0982, 0.0803, 0.0566, 0.0324, 0.0, 0.0857, 0.0444, 0.0401, 0.0521, 0.1264, 0.2112, 0.3098, 0.3991, 0.4764, 0.2397, 0.249, 0.228, 0.2283, 0.1631, 0.1755, 0.1556, 0.1488, 0.0996, 0.096, 0.0779, 0.0521, 0.0714, 0.0332, 0.0301, 0.0]']\n"
     ]
    }
   ],
   "source": [
    "# 测试\n",
    "test_data = MnistData(\"handtest.csv\")\n",
    "test_data.data_show(18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "438ee347",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hand = test_data[20][0]\n",
    "output = c.forward(hand)\n",
    "test_data[20][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "702f16ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x271c51f1c50>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(output.detach().numpy()).plot(kind=\"bar\", legend=False, ylim=(0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf25ef5f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
